Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, and Amit Sheth, 'Trust Networks', In: 5th Indian International Conference on Artificial Intelligence (IICAI-11), December 14-16, 2011 (invited tutorial).
We looked at the data. Here’s a breakdown of some key statistics about the nation’s incoming presidents’ addresses, how long they spoke, how well, and more.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/emoji
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
Krishnaprasad Thirunarayan, Pramod Anantharam, Cory Henson, and Amit Sheth, 'Trust Networks', In: 5th Indian International Conference on Artificial Intelligence (IICAI-11), December 14-16, 2011 (invited tutorial).
We looked at the data. Here’s a breakdown of some key statistics about the nation’s incoming presidents’ addresses, how long they spoke, how well, and more.
My books- Hacking Digital Learning Strategies http://hackingdls.com & Learning to Go https://gum.co/learn2go
Resources at http://shellyterrell.com/emoji
Artificial intelligence (AI) is everywhere, promising self-driving cars, medical breakthroughs, and new ways of working. But how do you separate hype from reality? How can your company apply AI to solve real business problems?
Here’s what AI learnings your business should keep in mind for 2017.
Study: The Future of VR, AR and Self-Driving CarsLinkedIn
We asked LinkedIn members worldwide about their levels of interest in the latest wave of technology: whether they’re using wearables, and whether they intend to buy self-driving cars and VR headsets as they become available. We asked them too about their attitudes to technology and to the growing role of Artificial Intelligence (AI) in the devices that they use. The answers were fascinating – and in many cases, surprising.
This SlideShare explores the full results of this study, including detailed market-by-market breakdowns of intention levels for each technology – and how attitudes change with age, location and seniority level. If you’re marketing a tech brand – or planning to use VR and wearables to reach a professional audience – then these are insights you won’t want to miss.
Krishnaprasad Thirunarayan, Trust Management: Multimodal Data Perspective,
Invited Tutorial, The 2015 International Conference on Collaboration
Technologies and Systems (CTS 2015), June 2015
PURGING OF UNTRUSTWORTHY RECOMMENDATIONS FROM A GRIDijngnjournal
In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.
Activate Networks Driving Collaboration Through Social Networks with Prof. Ro...Steven Wardell
Host: Steven Wardell, VP Marketing, Activate Networks
Introducer: Todd Kiernan, Director of ONA Solutions, Activate Networks
Presenter: Prof. Rob Cross, University of Virginia
C-DeepTrust A Context-Aware Deep Trust Prediction Model in Online Social Netw...OKOKPROJECTS
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
A computational dynamic trust model for user authorizationKamal Spring
Development of authorization mechanisms for secure information access by a large community of users in an open environment is an important problem in the ever-growing Internet world. In this paper we propose a computational dynamic trust model for user authorization, rooted in findings from social science. Unlike most existing computational trust models, this model distinguishes trusting belief in integrity from that in competence in different contexts and accounts for subjectivity in the evaluation of a particular trustee by different trusters. Simulation studies were conducted to compare the performance of the proposed integrity belief model with other trust models from the literature for different user behavior patterns. Experiments show that the proposed model achieves higher performance than other models especially in predicting the behavior of unstable users.
As the scope of current distributed computing model envisioned by the contemporary cloud computing environment enlarges to future federated Intercloud and ubiquitous & pervasive computing models such as Internet of Things (IoT), many difficult problems and challenges arise. Security is one of the most important and paramount concerns of such a computing environment. Current security mechanisms are very static, inflexible and not granular enough to make efficient and informed decisions in the Service Provider based computing environment. The conventional trust mechanisms in place are inadequate at addressing granular level trust issues in the highly distributed open environments. In this paper, we explore various Trust Management schemes and blueprints for enabling a framework that interested parties can use to determine the trustworthiness of disparate and heterogeneous computing entities. The paper also enumerates various business use case scenarios articulating how such a Trust Management framework would be highly invaluable for addressing the current as well as future computing environments needs.
A Computational Dynamic Trust Model for User Authorization1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
An exaustive survey of trust models in p2 p networkijwscjournal
Most of the peers accessing the services are under the assumption that the service accessed in a P2P
network is utmost secured. By means of prevailing hard security mechanisms, security goals like
authentication, authorization, privacy, non repudiation of services and other hard security issues are
resolved. But these mechanisms fail to provide soft security. An exhaustive survey of existing trust and
reputation models in P2P network regarding service provisioning is presented and challenges are listed.
Trust issues like trust bootstrapping, trust evidence procurement, trust assessment, trust interaction
outcome evaluation and other trust based classification of peer’s behavior into trusted,, inconsistent, un
trusted, malicious, betraying, redemptive are discussed,
Revision of Previous Show on SNA and Introduction to Tools
The Language of Networks
Introduction to Social Network Analysis/ Cases
Tools for Analyzing social networks, including graphing Facebook, LinkedIn, and Twitter networks
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
PURGING OF UNTRUSTWORTHY RECOMMENDATIONS FROM A GRIDijngnjournal
In grid computing, trust has massive significance. There is lot of research to propose various models in providing trusted resource sharing mechanisms. The trust is a belief or perception that various researchers have tried to correlate with some computational model. Trust on any entity can be direct or indirect. Direct trust is the impact of either first impression over the entity or acquired during some direct interaction. Indirect trust is the trust may be due to either reputation gained or recommendations received from various recommenders of a particular domain in a grid or any other domain outside that grid or outside that grid itself. Unfortunately, malicious indirect trust leads to the misuse of valuable resources of the grid. This paper proposes the mechanism of identifying and purging the untrustworthy recommendations in the grid environment. Through the obtained results, we show the way of purging of untrustworthy entities.
Activate Networks Driving Collaboration Through Social Networks with Prof. Ro...Steven Wardell
Host: Steven Wardell, VP Marketing, Activate Networks
Introducer: Todd Kiernan, Director of ONA Solutions, Activate Networks
Presenter: Prof. Rob Cross, University of Virginia
C-DeepTrust A Context-Aware Deep Trust Prediction Model in Online Social Netw...OKOKPROJECTS
https://okokprojects.com/
IEEE PROJECTS 2023-2024 TITLE LIST
WhatsApp : +91-8144199666
From Our Title List the Cost will be,
Mail Us: okokprojects@gmail.com
Website: : https://www.okokprojects.com
: http://www.ieeeproject.net
Support Including Packages
=======================
* Complete Source Code
* Complete Documentation
* Complete Presentation Slides
* Flow Diagram
* Database File
* Screenshots
* Execution Procedure
* Video Tutorials
* Supporting Softwares
Support Specialization
=======================
* 24/7 Support
* Ticketing System
* Voice Conference
* Video On Demand
* Remote Connectivity
* Document Customization
* Live Chat Support
A computational dynamic trust model for user authorizationKamal Spring
Development of authorization mechanisms for secure information access by a large community of users in an open environment is an important problem in the ever-growing Internet world. In this paper we propose a computational dynamic trust model for user authorization, rooted in findings from social science. Unlike most existing computational trust models, this model distinguishes trusting belief in integrity from that in competence in different contexts and accounts for subjectivity in the evaluation of a particular trustee by different trusters. Simulation studies were conducted to compare the performance of the proposed integrity belief model with other trust models from the literature for different user behavior patterns. Experiments show that the proposed model achieves higher performance than other models especially in predicting the behavior of unstable users.
As the scope of current distributed computing model envisioned by the contemporary cloud computing environment enlarges to future federated Intercloud and ubiquitous & pervasive computing models such as Internet of Things (IoT), many difficult problems and challenges arise. Security is one of the most important and paramount concerns of such a computing environment. Current security mechanisms are very static, inflexible and not granular enough to make efficient and informed decisions in the Service Provider based computing environment. The conventional trust mechanisms in place are inadequate at addressing granular level trust issues in the highly distributed open environments. In this paper, we explore various Trust Management schemes and blueprints for enabling a framework that interested parties can use to determine the trustworthiness of disparate and heterogeneous computing entities. The paper also enumerates various business use case scenarios articulating how such a Trust Management framework would be highly invaluable for addressing the current as well as future computing environments needs.
A Computational Dynamic Trust Model for User Authorization1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
Data at the Speed of Business with Data Mastering and GovernanceDATAVERSITY
Do you ever wonder how data-driven organizations fuel analytics, improve customer experience, and accelerate business productivity? They are successful by governing and mastering data effectively so they can get trusted data to those who need it faster. Efficient data discovery, mastering and democratization is critical for swiftly linking accurate data with business consumers. When business teams can quickly and easily locate, interpret, trust, and apply data assets to support sound business judgment, it takes less time to see value.
Join data mastering and data governance experts from Informatica—plus a real-world organization empowering trusted data for analytics—for a lively panel discussion. You’ll hear more about how a single cloud-native approach can help global businesses in any economy create more value—faster, more reliably, and with more confidence—by making data management and governance easier to implement.
An exaustive survey of trust models in p2 p networkijwscjournal
Most of the peers accessing the services are under the assumption that the service accessed in a P2P
network is utmost secured. By means of prevailing hard security mechanisms, security goals like
authentication, authorization, privacy, non repudiation of services and other hard security issues are
resolved. But these mechanisms fail to provide soft security. An exhaustive survey of existing trust and
reputation models in P2P network regarding service provisioning is presented and challenges are listed.
Trust issues like trust bootstrapping, trust evidence procurement, trust assessment, trust interaction
outcome evaluation and other trust based classification of peer’s behavior into trusted,, inconsistent, un
trusted, malicious, betraying, redemptive are discussed,
Revision of Previous Show on SNA and Introduction to Tools
The Language of Networks
Introduction to Social Network Analysis/ Cases
Tools for Analyzing social networks, including graphing Facebook, LinkedIn, and Twitter networks
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Let's dive deeper into the world of ODC! Ricardo Alves (OutSystems) will join us to tell all about the new Data Fabric. After that, Sezen de Bruijn (OutSystems) will get into the details on how to best design a sturdy architecture within ODC.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
1. Trust Networks
Krishnaprasad Thirunarayan (T. K. Prasad)
Professor, Department of Computer Science and Engineering
Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, OH-45435
(Collaborators: Pramod Anantharam, Cory Henson, and Professor Amit Sheth)
CTS-2012
May 21, 2012 Trust Networks: T. K. Prasad 1
2. Broad Outline
• Real-life Motivational Examples (Why?)
• Trust : Characteristics and Related Concepts (What?)
• Trust Ontology (What?)
– Type, Value, Process, Scope
• Gleaning Trustworthiness (How?) + Robustness to Attack
– Practical Examples of Trust Metrics
– Comparative Analysis of Bayesian Approaches to Trust
• Research Challenges (Why-What-How?)
– Sensor Networks
– Social Networks
– Interpersonal
• Details of Bayesian Approach to Multi-level Trust
May 21, 2012 Trust Networks: T. K. Prasad 2
4. Interpersonal
• With which neighbor should we leave our
children over the weekend when we are
required to be at the hospital?
• Who should be named as a guardian for our
children in the Will?
May 21, 2012 Trust Networks: T. K. Prasad 4
5. Social
• In Email:
– SUBJECT: [TitanPad] Amit Sheth invited you to an
EtherPad document.
– CONTENT: View it here:
http://knoesis.titanpad.com/200
• Issue: Is the request genuine or a trap?
May 21, 2012 Trust Networks: T. K. Prasad 5
6. Social
• To click or not to click a http://bit.ly-URL
• To rely or not to rely on a product review
(when only a few reviews are present, or the
reviews are conflicting)?
May 21, 2012 Trust Networks: T. K. Prasad 6
7. Sensors
• Weather sensor network predicts a potential tornado in
the vicinity of a city.
• Issue: Should we mobilize emergency response teams
ahead of time?
• Van’s TCS (Traction Control System) indicator light came
on intermittently, while driving.
• Issue: Which is faulty: the indicator light or the traction
control system?
• Van’s Check Engine light came on, while driving.
• Issue: Which is faulty: the indicator light or the
transmission?
May 21, 2012 Trust Networks: T. K. Prasad 7
8. Man-Machine Hybrid
Collaborative Systems
The 2002 Uberlingen Mid-air Collision (between
Bashkirian Airlines Flight 2937 and DHL Flight
611) occurred because the pilot of one of the
planes trusted the human air traffic controller
(who was ill-informed about the unfolding
situation), instead of the electronic TCAS system
(which was providing conflicting but correct
course of action to avoid collision).
http://en.wikipedia.org/wiki/2002_Uberlingen_mid-air_collision
May 21, 2012 Trust Networks: T. K. Prasad 8
9. Man-Machine Hybrid
Collaborative Systems
In hybrid situations, artificial agents
should reason about the
trustworthiness and deceptive
actions of their human counter
parts. People and agents in virtual
communities will deceive, and will
be deceived. Castelfranchi and Tan, 2002
May 21, 2012 Trust Networks: T. K. Prasad 9
10. Common Issues and Context
• Uncertainty
– About the validity of a claim or
assumption
– Past Experience : Vulnerability
• Need for action
• Critical decision with potential for loss
May 21, 2012 Trust Networks: T. K. Prasad 10
11. Commonality among Trust Definitions*
• a Trustor
– someone who must choose whether, and how much, to
trust
• a Trustee
– someone or something that is to be trusted
• an Action
– by which the trustor is choosing to be vulnerable to the
trustee based on an assessment of trustee’s nature
• a Context
– in which the potential negative consequences of betrayal
outweigh any perceived positive results.
*http://www.iarpa.gov/rfi_trust.html
May 21, 2012 Trust Networks: T. K. Prasad 11
12. Sources of uncertainty
• In social network, trustor’s incomplete
knowledge or trustee’s devious intentions.
• In sensor network, unpredictable environment
(e.g., random phenomenon), or sensor faults
(e.g., due to aging), leading to corrupt data.
• In cognitive radio networks, unpredictable
channel noise or traffic.
• Interpersonal examples
– Irresponsible / selfish / greedy / vengeful traits
May 21, 2012 Trust Networks: T. K. Prasad 12
13. Why Track Trust?
• In Mobile Ad Hoc Networks (MANETs),
trust enables dynamic determination of
secure routes.
– Efficiency: To improve throughput
• By avoiding nodes facing bad channel condition
– Robustness : To detect malicious nodes
• When attackers enter the network in spite of
secure key distribution/authentication
May 21, 2012 Trust Networks: T. K. Prasad 13
14. Why Track Trust?
• In sensor networks, it allows detection of
faults and transient bad behaviors due to
environmental effects.
• In cognitive radio networks, it can enable
selection of optimal channel (less noisy,
less crowded channels).
May 21, 2012 Trust Networks: T. K. Prasad 14
15. Why Track Trust?
• In E-commerce:
–To predict future behavior in a
reliable manner.
–To incentivize “good” behavior and
discourage “bad” behavior.
–To detect malicious entities.
May 21, 2012 Trust Networks: T. K. Prasad 15
16. The Two Sides of Trust
• Trustor assesses trustee for
dependability.
• Trustee casts itself in positive light to
trustor.
• Trust is a function of trustee's
perceived trustworthiness and the
trustor's propensity to trust.
May 21, 2012 Trust Networks: T. K. Prasad 16
17. Risk/uncertainty mitigation
• Compensating factors that alter trust
threshold
• In e-commerce, warranties and insurance
reduce risk.
• In sensor networks, redundancy enables
filtering of corrupt data.
• In interpersonal situations, close ties
help.
May 21, 2012 Trust Networks: T. K. Prasad 17
18. Trust and Related Concepts
(What is trust?)
May 21, 2012 Trust Networks: T. K. Prasad 18
19. Trust Definition : Psychology slant
Trust is the psychological state
comprising a willingness to be
vulnerable in expectation of a
valued result.
Ontology of Trust, Huang and Fox, 2006
Josang et al’s Decision Trust
May 21, 2012 Trust Networks: T. K. Prasad 19
20. Trust Definition : Psychology slant
Trust in a person is a commitment to
an action based on a belief that the
future actions of that person will
lead to good outcome.
Golbeck and Hendler, 2006
May 21, 2012 Trust Networks: T. K. Prasad 20
21. Trust Definition : Probability slant
Trust (or, symmetrically, distrust)
is a level of subjective probability
with which an agent assesses
that another agent will perform
a particular action, both before
and independently of such an
action being monitored …
Can we Trust Trust?, Diego Gambetta, 2000
Josang et al’s Reliability Trust
May 21, 2012 Trust Networks: T. K. Prasad 21
22. Trustworthiness Definition :
Psychology Slant
Trustworthiness is a collection of
qualities of an agent that leads them
to be considered as deserving of
trust from others (in one or more
environments, under different
conditions, and to different degrees).
http://www.iarpa.gov/rfi_trust.html
May 21, 2012 Trust Networks: T. K. Prasad 22
23. Trustworthiness Definition :
Probability slant
Trustworthiness is the objective
probability that the trustee
performs a particular action on
which the interests of the
trustor depend.
Solhaug et al, 2007
May 21, 2012 Trust Networks: T. K. Prasad 23
24. Trust vs Trustworthiness : My View
Trust Disposition
Depends on
Potentially Quantified Trustworthiness Qualities
+
Context-based Trust Threshold
E.g.*, In the context of trusting strangers, people in
the West will trust for lower levels of trustworthiness
than people in the Gulf. *Bohnet et al, 5/2010
May 21, 2012 Trust Networks: T. K. Prasad 24
25. Qualitative Trust Dispositions
(depends on Trust Threshold)
http://paulenglish.com/trust.html
May 21, 2012 Trust Networks: T. K. Prasad 25
26. (Community-based) Reputation
• Reputation* is the community or public
estimation of standing for merit,
achievement, reliability, etc. *dictionary.com
• Reputation** is the opinion (or a social
evaluation) of a community toward a
person, a group of people, or an
organization on a certain criterion. **Wikipedia
• Cf. Brand-value, PageRank, eBay profile, etc.
May 21, 2012 Trust Networks: T. K. Prasad 28
27. Trust vs. (Community-based)
Reputation
Reputation can be a basis for trust.
However, they are different notions*.
• I trust you because of your good reputation.
• I trust you despite your bad reputation.
• Do you still trust Toyota brand?
*Josang et al, 2007
May 21, 2012 Trust Networks: T. K. Prasad 29
28. Trust vs. (Community-based)
Reputation
Trust :: Reputation
::::
Local :: Global
::::
Subjective :: Objective
(Cf. Security refers to resistance to attacks.)
May 21, 2012 Trust Networks: T. K. Prasad 30
29. Reputation is Overloaded
Community-based Reputation
vs.
Temporal Reputation
(Cf. Sustained good behavior over time elicits
temporal reputation-based trust.)
May 21, 2012 Trust Networks: T. K. Prasad 31
30. Trust is well-known,
but is not well-understood.
The utility of a notion
testifies not to its clarity but
rather to the philosophical
importance of clarifying it.
-- Nelson Goodman
(Fact, Fiction and Forecast, 1955)
May 21, 2012 Trust Networks: T. K. Prasad 34
31. Trust Ontology
(What is trust?)
Illustration of Knowledge Representation and Reasoning:
Relating Semantics to Data Structures and Algorithms
May 21, 2012 Trust Networks: T. K. Prasad 35
32. Example Trust Network -
Different Trust Links with Local Order on out-links
• Alice trusts Bob for recommending good car
mechanic.
• Bob trusts Dick to be a good car mechanic.
• Charlie does not trust Dick to be a good car
mechanic.
• Alice trusts Bob more than Charlie, for
recommending good car mechanic.
• Alice trusts Charlie more than Bob, for
recommending good baby sitter.
*Thirunarayan et al, IICAI 2009
May 21, 2012 Trust Networks: T. K. Prasad 36
33. Digression: Illustration of Knowledge
Representation and Reasoning
• Abstract and encode clearly delineated “subarea”
of knowledge in a formal language.
– Trust Networks => node-labeled, edge-labeled
directed graph (DATA STRUCTURES)
• Specify the meaning in terms of how “network
elements” relate to or compose with each other.
– Semantics of Trust, Trust Metrics => using logic or
probabilistic basis, constraints, etc. (SEMANTICS)
• Develop efficient graph-based procedures
– Trust value determination/querying (INFERENCE
ALGORITHMS)
May 21, 2012 Trust Networks: T. K. Prasad 37
34. (In recommendations)
(For capacity to act)
(For lack of
capacity to act)
May 21, 2012 Trust Networks: T. K. Prasad 38
35. Trust Ontology*
6-tuple representing a trust relationship:
{type, value, scope, process}
trustor trustee
Type – Represents the nature of trust relationship.
Value – Quantifies trustworthiness for comparison.
Scope – Represents applicable context for trust.
Process – Represents the method by which the value is
created and maintained.
*Anantharam et al, NAECON 2010
May 21, 2012 Trust Networks: T. K. Prasad 39
36. Trust Ontology:
Trust Type, Trust Value, and Trust Scope
Trust Type*
Referral Trust – Agent a1 trusts agent a2’s ability to
recommend another agent.
(Non-)Functional Trust – Agent a1 (dis)trusts agent a2’s
ability to perform an action.
Cf. ** trust in belief vs. trust in performance
Trust Value
E.g., Star rating, numeric rating, or partial ordering.
Trust Scope*
E.g., Car Mechanic context. *Thirunarayan et al, IICAI 2009
** Huang and Fox, 2006
May 21, 2012 Trust Networks: T. K. Prasad 40
37. Multidimensional / Orthogonal
Trust Scopes in Ecommerce
• Trust in a vendor to deliver on
commitments
• Trust in vendor's ethical use of
consumer data
• Trust in Internet communication being
secure.
• Plus: Propensity/Disposition to trust
May 21, 2012 Trust Networks: T. K. Prasad 41
38. Trust Ontology:
Trust Process
Represents the method by which the value
is computed and maintained.
Primitive (for functional and referral links)*
Reputation – based on past behavior (temporal) or
community opinion.
Policy – based on explicitly stated constraints.
Evidence – based on seeking/verifying evidence.
Provenance – based on lineage information.
Composite (for admissible paths)**
Propagation (Chaining and Aggregation)
*Anantharam et al, NAECON 2010
**Thirunarayan et al, IICAI 2009
May 21, 2012 Trust Networks: T. K. Prasad 44
40. Example Trust Network illustrating Ontology Concepts
Dick is a
Bob is a car certified
aficionado mechanic
type: referral
process: reputation
scope: car mechanic
value: TAB Bob type: functional
process: policy
TAB > TAC scope: car mechanic ASE certified
value: 10
type: referral
Alice
process: reputation
scope: car mechanic type: non-functional
value: TAC process: reputation
scope: car mechanic Dick
value: 3
47
Charlie
41. Unified Illustration of Trust Processes
Scenario : Hiring Web Search Engineer - An R&D Position
Various Trust Processes :
• (Temporal-based) Reputation: Past job experiences
• (Community-based) Reputation: Multiple
references
• Policy-based: Score cutoffs on screening test
• Provenance-based: Department/University of
graduation
• Evidence-based: Multiple interviews (phone, on-
site, R&D team)
May 21, 2012 Trust Networks: T. K. Prasad 48
42. Deception
• Deception is the betrayal of trust.
–Ironically, trust makes us prone to
deception.
–Knowing what features are used to
glean trustworthiness can also assist
in avoiding detection while deceiving.
May 21, 2012 Trust Networks: T. K. Prasad 49
43. Gleaning Trustworthiness :
Practical Examples
(How to determine trustworthiness?)
May 21, 2012 Trust Networks: T. K. Prasad 50
44. Trust Metric and Trust Model
• Trust Metric => How is primitive trust
represented?
• E.g., Real number, Finite levels, Partial Order, Opinion =
(belief, disbelief, uncertainty), etc.
• Trust Model => How is composite trust
computed or propagated?
Y. L. Sun, et al, 2/2008
May 21, 2012 Trust Networks: T. K. Prasad 51
45. Ideal Approach
• Capture semantics of trust using
– axioms for trust propagation, or
– catalog of example trust networks that are
equivalent.
• Develop trust computation rules for
propagation (that is, chaining and
aggregation) that satisfy the axioms or
equivalence relation.
May 21, 2012 Trust Networks: T. K. Prasad 52
46. Direct Trust : Functional and Referral
Reputation-based Process
(Using large number of observations)
May 21, 2012 Trust Networks: T. K. Prasad 53
47. Reputation-based Frameworks
• Centralized Trust Authority
– E.g., E-commerce systems, etc.
• Distributed Trust Representation and
Computation (using Bayesian analytics)
– E.g., MANETs, peer-to-peer networks, etc.
May 21, 2012 Trust Networks: T. K. Prasad 54
48. Using Large Number of Observations
• Over time (<= Referral + Functional) :
Temporal Reputation-based Process
– Mobile Ad-Hoc Networks
– Sensor Networks
• Quantitative information
(Numeric data)
• Over agents (<= Referral + Functional) :
Community Reputation-based Process
– Product Rating Systems
• Quantitative + Qualitative information
(Numeric + text data)
May 21, 2012 Trust Networks: T. K. Prasad 55
49. Desiderata for Trustworthiness
Computation Function
• Initialization Problem : How do we get initial value?
• Update Problem : How do we reflect the observed
behavior in the current value dynamically?
• Trusting Trust* Issue: How do we mirror uncertainty
in our estimates as a function of observations?
• Law of Large Numbers: The average of the results obtained from a
large number of trials should be close to the expected value.
• Efficiency Problem : How do we store and update
values efficiently?
*Ken Thompson’s Turing Award Lecture: “Reflections on Trusting Trust”
May 21, 2012 Trust Networks: T. K. Prasad 56
50. Mathematical Background
Beta PDF for Reputation
May 21, 2012 Trust Networks: T. K. Prasad 57
51. Beta-distribution : Gently
• Consider a (potentially unfair) coin that comes up
HEADS with probability p and TAILS with probability
(1 – p).
• Suppose we perform ( r + s ) coin tosses and the coin
turns up with HEADS r times and with TAILS s times.
• What is the best estimate of the distribution of the
probability p given these observations?
=> Beta-distribution with parameters ( r+1, s+1 )
f(p; r+1, s+1)
May 21, 2012 Trust Networks: T. K. Prasad 58
52. Beta Probability Density Function(PDF)
x is a probability,
so it ranges from 0-1
If the prior distribution of x is
uniform, then the beta
distribution gives posterior
distribution of x after
observing a-1 occurrences
of event with probability x
and b-1 occurrences of the
complementary event with
probability (1-x).
May 21, 2012 Trust Networks: T. K. Prasad 59
53. a = b, so the pdf’s are symmetric w.r.t 0.5.
Note that the graphs get narrower as (a+b) increases.
a= 2
a= 1
b= 2
b= 1
a= 5 a= 10
b= 5 b= 10
May 21, 2012 Trust Networks: T. K. Prasad 60
54. a ≠ b, so the pdf’s are asymmetric w.r.t . 0.5.
Note that the graphs get narrower as (a+b) increases.
a= 5 a= 5
b= 25 b= 10
a= 25 a= 10
b= 5 b= 5
May 21, 2012 Trust Networks: T. K. Prasad 61
55. Beta-distribution - Applicability
• Dynamic trustworthiness can be
characterized using beta probability
distribution function gleaned from total
number of correct (supportive) r = (a-1)
and total number of erroneous
(opposing) s = (b-1) observations so far.
• Overall trustworthiness (reputation) is its
mean: a/a +b
May 21, 2012 Trust Networks: T. K. Prasad 63
56. Why Beta-distribution?
• Intuitively satisfactory, Mathematically precise, and
Computationally tractable
• Initialization Problem : Assumes that all probability values
are equally likely.
• Update Problem : Updates (a, b) by incrementing a for
every correct (supportive) observation and b for every
erroneous (opposing) observation.
• Trusting Trust Issue: The graph peaks around the mean, and
the variance diminishes as the number of observations
increase, if the agent is well-behaved.
• Efficiency Problem: Only two numbers stored/updated.
May 21, 2012 Trust Networks: T. K. Prasad 64
57. Information Theoretic Interpretation
of Trustworthiness Probability
• Intuitively, probability values of 0 and 1 imply
certainty, while probability value of 0.5 implies
a lot of uncertainty.
• This can be formalized by mapping probability
in [0,1] to trust value in [–1,1], using
information theoretic approach.
Y. L. Sun, et al, 2/2008
May 21, 2012 Trust Networks: T. K. Prasad 65
58. Information Theoretic Interpretation
of Trustworthiness Probability
• T(trustee : trustor, action) =
if 0.5 <= p
then 1 – H(p) /* 0.5 <= p <= 1 */
else H(p) – 1 /* 0 <= p <= 0.5 */
where
H(p) = – p log2(p) – (1 – p) log2(1 – p)
May 21, 2012 Trust Networks: T. K. Prasad 66
59. Plot of T(trustee : trustor, action) vs. p
Trust portion (p in [0.5,1]) Distrust portion (p in [0,0.5])
May 21, 2012 Trust Networks: T. K. Prasad 68
60. Linear vs Nonlinear Map
Relative to computing trust as
T(trustee : trustor, action) = ( p – 0.5 ) * 2
to map of trust probability p in [0,1] to a
trust value in [-1,+1], the information
theoretic formulation yields a non-linear
map that amplifies the effect of changes
to trust probability on the trust value at
the extremes.
May 21, 2012 Trust Networks: T. K. Prasad 69
61. Direct Trust : Functional
Policy-based Process
(Using Trustworthiness Qualities)
May 21, 2012 Trust Networks: T. K. Prasad 70
62. General Approach to Trust Assessment
• Domain dependent qualities for determining
trustworthiness
– Based on Content / Data
– Based on External Cues / Metadata
• Domain independent mapping to trust values
or levels
– Quantification through abstraction and
classification
May 21, 2012 Trust Networks: T. K. Prasad 71
63. Example: Wikipedia Articles
• Quality (content-based)
– Appraisal of information provenance
• References to peer-reviewed publication
• Proportion of paragraphs with citation
– Article size
• Credibility (metadata-based)
– Author connectivity
– Edit pattern and development history
• Revision count
• Proportion of reverted edits - (i) normal (ii) due to vandalism
• Mean time between edits
• Mean edit length.
Sai Moturu, 8/2009
May 21, 2012 Trust Networks: T. K. Prasad 72
64. (cont’d)
• Quantification of Trustworthiness
– Based on Dispersion Degree Score
(Extent of deviation from mean)
• Evaluation Metric
– Ranking based on trust level (determined from
trustworthiness scores), and compared to gold
standard classification using Normalized
Discounted Cumulative Gain (NDCG)
• RATINGS: featured, good, standard, cleanup, and stub.
• NDCG: penalizes more heavily errors at the top.
May 21, 2012 Trust Networks: T. K. Prasad 73
65. Example: Websites
• Trustworthiness estimated based on criticality
of data exchanged.
• Email address / Username / password
• Phone number / Home address
• Date of birth
• Social Security Number / Bank Account Number
• Intuition: A piece of data is critical if and only
if it is exchanged with a small number of
highly trusted sites.
May 21, 2012 Trust Networks: T. K. Prasad 74
66. Indirect Trust : Referral + Functional
Variety of Trust Metrics and Models
(Using Propagation – Chaining and Aggregation over Paths)
May 21, 2012 Trust Networks: T. K. Prasad 75
67. Collaborative Filtering
• Collaborative Filtering: Item-rating by a user
predicted on the basis of user’s similarity to
other users.
• Similarity Measures:
• Profile-based
• Item-ratings-based
• Item-category-based
May 21, 2012 Trust Networks: T. K. Prasad 78
68. Collaborative Filtering
• Pros:
– Items-agnostic
– Scales well over time with large number of items
• Cons:
– Data Sparsity Problem: Small number of
common items between users.
– Cold Start Users: Small number of items rated
by a user.
– Prone to Copy-Profile Attack: An attacker can
create a targeted-user-like profile to
manipulate recommendations.
Massa-Avesani, 2007
May 21, 2012 Trust Networks: T. K. Prasad 79
69. Trust-aware Recommender System
• TaRS uses explicit/direct trust between users to
predict implicit/indirect trust between users
through chaining.
• Collaborative Filtering Limitations Overcome:
– Mitigates Data Sparsity: Trust propagation is more
general and improves coverage.
– Bootstraps Cold Start Users: A single trust link from a
new user can enable the user to inherit several
“parental” recommendations.
– Robust w.r.t Copy-Profile Attack: Fake identities are
not trusted by an active user.
Massa-Avesani, 2007
May 21, 2012 Trust Networks: T. K. Prasad 80
70. Trust Propagation Frameworks
• Chaining, Aggregation, and Overriding
Golbeck – Hendler, 2006 Massa-Avesani, 2005 Sun et al, 2006
Bintzios et al, 2006 Thirunarayan et al, 2009
• Trust Management Richardson et al, 2003
• Abstract properties of operators
• Reasoning with trust Guha et al., 2004
• Matrix-based trust propagation
• The Beta-Reputation System
• Algebra on opinion = (belief, disbelief, uncertainty)
Josang and Ismail, 2002
May 21, 2012 Trust Networks: T. K. Prasad 81
71. Trust Propagation Algorithms
• Top-down
• 1: Extract trust DAG (eliminate cycles)
• 2: Predict trust score for a source in a target by
aggregating trust scores in target inherited
from source’s “trusted” parents weighted with
trust value in the corresponding parent.
–Computation is level-by-level
–Alternatively, computation can be based on
paths.
Golbeck – Hendler, 2006
May 21, 2012 Trust Networks: T. K. Prasad 82
72. Trust Propagation Algorithms
• Bottom-up
• 1: Extract trust DAG (eliminate cycles)
• 2: Predict trust score for a source in a target by
aggregating trust scores in target inherited
from target’s “trusted” neighbors weighted
with trust value in the corresponding neighbor.
–Computation is level-by-level
–Alternatively, computation can be based on
paths. Massa-Avesani, 2005
Bintzios et al, 2006
May 21, 2012 Trust Networks: T. K. Prasad 83
73. Top-down vs Bottom-up (visualized)
Target
w1 w3
Target w2
Trust-Network
w1 w2 w3
source source
May 21, 2012 Trust Networks: T. K. Prasad 84
74. Example: Comparative Analysis
Different Interpretation:
q distrusts s (Bintzios et al’s)
vs
q has no information about
the trustworthiness of s (our’s,
Golbeck rounding algorithm)
May 21, 2012 Trust Networks: T. K. Prasad 85
75. Example: Well-founded
Cyclic Trust Network
Thirunarayan and Verma, 2007
May 21, 2012 Trust Networks: T. K. Prasad 87
77. Indirect Trust : Referral + Functional
Variety of Bayesian Trust Models
With Applications to Mobile Ad hoc Networks
Wireless Sensor Networks, etc.
May 21, 2012 Trust Networks: T. K. Prasad 89
78. Direct Trust : Functional and Referral
• Trust link: {subject : agent, action}
• For MANETs (resp. cognitive radio)
– Functional => Packet Forwarding (resp. quality of
spectrum / channel)
– Referral => Recommendations
Based on beta-reputation model:
Probability for trust = (S + 1) / (S + F +2)
where S = Number of good actions
F = Number of bad actions
May 21, 2012 Trust Networks: T. K. Prasad 90
79. Direct Trust : Functional and Referral
• Direct Trust for Packet Forwarding
• S = Number of packets forwarded
• F = Number of packets dropped
• S + F = Total number of requests for packet forwarding
• Direct Trust for Recommendations
• S = Number of times observed direct trust for packet
forwarding approximates expected indirect trust for
packet forwarding (trust over transit path : r+f)
• F = Number of times observed direct trust for packet
forwarding does not approximate expected indirect
trust for packet forwarding (trust over transit path : r+f)
May 21, 2012 Trust Networks: T. K. Prasad 91
80. Indirect Trust : Functional and Referral
• Indirect Trust for Packet Forwarding
– Used when direct trust is not available
»(overriding behavior)
• Chain links for a path from a recommender to the target
– Multiplicative
• Aggregate over multiple (parallel) paths from
recommenders to the target
– Unclear, in general
• Indirect Trust for Recommendations
• Obtained implicitly through computed referral trust
May 21, 2012 Trust Networks: T. K. Prasad 92
81. Trust Propagation Rules :
Axioms for Trust Models
Rule 1: Concatenation
propagation does
not increase trust. |T(A1,C1)| <= min(|R(A1,B1)|, |T(B1,C1)|)
Rule 2: Multipath
propagation does
not reduce trust. 0<=T(A1,C1) <= T(A2,C2) for R1 > 0 and T2 >= 0
0>=T(A1,C1) >= T(A2,C2) for R1 > 0 and T2 < 0
May 21, 2012 Trust Networks: T. K. Prasad
Sun et al, 2006 93
82. (cont’d)
Rule 3: Trust based on multiple referrals from a
single source should not be higher than that
from independent sources.
0<=T(A1,C1) <= T(A2,C2) for R1, R2, R3 > 0 and T2 >= 0
0>=T(A1,C1) >= T(A2,C2) for R1, R2, R3 > 0 and T2 < 0
May 21, 2012 Trust Networks: T. K. Prasad
Sun et al, 2006 94
83. Trust Propagation Rules :
Implementation
1 2
May 21, 2012 Trust Networks: T. K. Prasad
Sun et al, 2006 95
84. Trust Paths Visualized for Scalability:
Semantics unclear based on Sun et al’s spec
Bottom-up computation
reflects our needs better?
May 21, 2012 Trust Networks: T. K. Prasad 101
85. Trust : Functional and Referral
• Direct Trust for Primitive Actions based on
• S = Number of success actions
• F = Number of failed actions
• S + F = Total number of actions
• Indirect Trust via Recommendations based on
summing direct experiences of recommenders
• Sk = Number of success actions for kth recommender
• Fk = Number of failed actions for kth recommender
• No chaining for referrals Denko-Sun 2008
May 21, 2012 Trust Networks: T. K. Prasad 104
86. Cumulative Trust using
Direct Experience and Recommendations
• Cumulative Trust is obtained by using total
number of success actions and failed actions
from direct experience (ns,nu) and from i
(indirect experiences through)
recommendations (nsr,nur).
May 21, 2012 Trust Networks: T. K. Prasad 105
87. Contents of [Ganeriwal et al, 2007] Paper
• (a,b)-parameters to compute trust of i in j is
obtained by combining direct observations
(aj,bj) with indirect observations (ajk,bjk) from
k weighted by (ak,bk) using [Josang-Ismail,
2002] chaining/discounting rule.
• Obtains cumulative trust by combining direct trust from
a functional link and indirect trusts using paths
containing one referral link and one functional link.
• However, it does not distinguish functional and referral
trust.
May 21, 2012 Trust Networks: T. K. Prasad 106
88. Trust Propagation Rules :
Beta Reputation System
1 2
May 21, 2012 Trust Networks: T. K. Prasad
Josang and Ismail, 2002
107
89. Trust Propagation Rules :
Beta Reputation System
1 2
Rule 1: Concatenation propagation (reputation
discounting)
r = 2*r1 * r2 / (s1+2)(r2+s2+2)+2*r1
s = 2*r1 * s2 / (s1+2)(r2+s2+2)+2*r1
Rule 2: Multipath propagation (combining
feedback)
r = r1 + r2 s = s1 + s2
Rule *: Temporal Decay (Forgetting)
May 21, 2012 Trust Networks: T. K. Prasad
Josang and Ismail, 2002
108
90. Contents of [B-Trust, 2006] Paper
• Uses generic K-level discrete trust metric (as
opposed to 2-level metric)
– E.g., (very untrustworthy, untrustworthy, trustworthy, very trustworthy)
– E.g., reminiscent of Amazon recommendation ratings
• Distributed (local), robust, lightweight,
computational trust that takes into account
context, subjectivity, and time
– a la reputation-based approach
• Application: Pervasive computing
May 21, 2012 Trust Networks: T. K. Prasad 109
91. BUG -> FLAWED LEARNING?
• The approach does not clearly separate the
use of stable background knowledge for
applying Bayes’ rule, from the need to
dynamically learn background knowledge for
gleaning trust from experience.
• As a result, the initial trust values do not
change in response to experience.
May 21, 2012 Trust Networks: T. K. Prasad 110
92. Comparative Analysis
[Menko-T.Sun] : Beta-distribution based
• Direct functional trust and indirect functional
trust (through direct referrals).
– Trivial chaining.
– One fixed context, local and distributed.
• Robustness improved by dropping extreme
recommendations, though recommenders not
distinguished.
May 21, 2012 Trust Networks: T. K. Prasad 111
93. Comparative Analysis
[Ganeriwal et al]: Beta-distribution based
• Functional and Referral trust mixed up.
– Context glossed over, local and distributed.
• Robustness improved by chaining trust links of
length 2, using Josang-Ismail opinion
composition.
– Recommenders distinguished.
– Chaining weighs recommendations by
recommender trust.
May 21, 2012 Trust Networks: T. K. Prasad 112
94. Comparative Analysis
[Y. Sun et al]: Beta-distribution based
• Functional and Referral trust separated.
• One Context, hybrid (dynamically formed trust
network) and distributed.
• Information-theoretic approach
• Axiomatic specification and implementation of
trust propagation (chaining and aggregation)
• Incomplete w.r.t. arbitrary trust networks
• Robustness and Quality improved by analyzing
dynamically formed trust network.
May 21, 2012 Trust Networks: T. K. Prasad 113
95. Comparative Analysis
[B-Trust et al]: Multi-valued trust - Bayesian
• Functional and Referral trust separated.
– Context-based, local and distributed.
• Individualized aggregation, trivial chaining.
• Nice roadmap for theory, specification, and
implementation of trust networks
– Multi-valued Trust evolution : novel but buggy.
May 21, 2012 Trust Networks: T. K. Prasad 114
96. Comparative Analysis
[MLT-Approach]: Multi-level trust using Dirichlet
Distribution
• Functional and Referral trust separated.
– Context-based, local and distributed.
• Individualized aggregation, trivial chaining.
• Based on B-Trust roadmap but MLT evolution
based on Dirichlet distribution: conceptually
satisfactory and computationally efficient
• Example-based analysis for insights
May 21, 2012 Trust Networks: T. K. Prasad 115
97. Security Issues:
Threats and Vulnerabilities
Attacks and Robustness Analysis
May 21, 2012 Trust Networks: T. K. Prasad 116
98. Attacks
• Trust Management is an attractive target for
malicious nodes.
Bad mouthing attack (Defamation)
Dishonest recommendations on good nodes (calling them
bad)
Ballot stuffing attack (Collusion)
Dishonest recommendations on bad nodes (calling them
good)
Sybil attack
Creating Fake Ids
Newcomer attack
Registering as new nodes
May 21, 2012 Trust Networks: T. K. Prasad 117
99. Attacks
• Inconsistency in time-domain
On-Off attack
Malicious node behaves good and bad alternatively to
avoid detection
Sleeper attack
Malicious node acquires high trust by behaving good
and then strikes by behaving bad
Inconsistency in node-domain
Conflicting Behavior Attack
Provide one recommendation to one set of peers and a
conflicting recommendation to a disjoint set of peers
May 21, 2012 Trust Networks: T. K. Prasad 118
100. Security : Robustness w.r.t Attacks
Bad mouthing attack
Example: Competent nodes downplay competitions.
Example: Can diminish throughput due to lost capacity.
Approach:
Separate functional and referral trust, updating
referral trust to track good recommendations
Trust composition rules ensure that low or
negative referral trust does not impact decision
Low trust nodes can be branded as malicious and
avoided. (Not viable if majority collude.)
May 21, 2012 Trust Networks: T. K. Prasad 119
101. Security : Robustness w.r.t Attacks
Ballot stuffing attack
Example: Malicious nodes collude to recommend each
other.
Example: Can cause unexpected loss of throughput.
Approach:
Feedback : Cross-check actual functional
performance with expected behavior via referral,
and update (reward/penalize) referral trust (in
parent) accordingly (in addition to updating
functional trust (in target))
May 21, 2012 Trust Networks: T. K. Prasad 120
102. Security : Robustness w.r.t. Attacks
Sybil attack
Create Fake Ids to take blame for malicious
behavior (dropping packets)
Newcomer attack
Register as new node to erase past history
Approach
Requires separate (key-based or security token-
based) authentication mechanism (with TTP) to
overcome these attacks.
May 21, 2012 Trust Networks: T. K. Prasad 121
103. Security : Robustness w.r.t Attacks
On-Off attack
Sleeper attack
Example: Due to malice or environmental changes
Approach:
Use forgetting factor (0<=b<=1):
k good/bad actions at t1
= k * b(t2 – t1) good/bad actions at t2 (> t1)
May 21, 2012 Trust Networks: T. K. Prasad 122
104. Forgetting Factor
k good/bad actions at t1 = k * b(t2 – t1) good/bad actions at t2 (> t1)
• High b value (0.9) enhances memorized time
window, while low b value (0.001) reduces it.
– High b enables malicious nodes (on-off/sleeper
attackers) to use prior good actions to mask
subsequent intentional bad actions.
• Reduces reliability.
– Low b forces legitimate nodes to be avoided due
to short spurts of unintentional bad actions.
• Reduces throughput.
May 21, 2012 Trust Networks: T. K. Prasad 123
105. Adaptive Forgetting Factor
• Intuition: Bad actions are remembered for a
longer duration than good actions.
• Actions performed with high trust forgotten quicker than
actions performed with low trust.
Choose b equal to ( 1 – p )
Choose b = 0.01 when p in [0.5,1] else 0.9
Example: Similar ideas used in Ushahidi
Note: Effectively, more good actions are
necessary to compensate for fewer bad actions,
to recover trust.
May 21, 2012 Trust Networks: T. K. Prasad 124
106. Security : Robustness w.r.t. Attacks
Conflicting Behavior Attack
Malicious node divide and conquer, by behaving
differently (resp. by providing different
recommendations) to different peers, causing
peers to provide conflicting recommendations to
source about the malicious node (resp. about
some target), reducing source’s referral trust in
some peers.
Eventually, this causes recommendations of some peers
to be ignored incorrectly.
May 21, 2012 Trust Networks: T. K. Prasad 125
107. Example
• Peer Node Set 1: 1, 2, 3, and 4
• Peer Node Set 2: 5, 6, 7, and 8
• Malicious node 0 behaves well towards nodes in
Set 1 but behaves badly towards nodes in Set 2.
• When node 9 seeks recommendations from
nodes in Set 1 U Set 2 on node 0, node 9 receives
conflicting recommendations on malicious node
0, causing referral trust in nodes in Set 1 or
nodes in Set 2 to be lowered.
=> Eventually throughput lowered
May 21, 2012 Trust Networks: T. K. Prasad 126
108. Security : Robustness w.r.t. Attacks
Conflicting Behavior Attack
Issue: Can recommenders get feedback
to reduce trust in malicious node?
Otherwise, referral trust cannot be
relied upon for detecting malicious
nodes.
May 21, 2012 Trust Networks: T. K. Prasad 127
109. Security : Robustness w.r.t. Attacks
If cumulative referral trust in B is computed using
direct experiences of several recommenders,
then it is possible to weed out extreme
experiences using deviation from the mean trust
value, where S is some chosen threshold.
May 21, 2012 Trust Networks: T. K. Prasad
Denko-Sun 2008 128
110. Contents of [Ganeriwal et al, 2007] Paper
• Combined trust of i in j is obtained from direct
observations (aj,bj) of j by i and indirect
observations (ajk,bjk) from k to i weighted by
(ak,bk) using [Josang-Ismail, 2002]
chaining/weighting/discounting rule.
• This discounting rule makes the local trust
computation resilient to bad mouthing ajk << bjk
and ballot stuffing ajk >> bjk attacks from
unreliable/malicious nodes ak << bk.
• It requires aging to be resilient to sleeper attacks.
May 21, 2012 Trust Networks: T. K. Prasad 129
111. APPROACH/ Trust Type / Trust Model / Robustness to
METRIC Context Foundation Attacks
D[3] / Functional / One Trivial chaining / Ballot-stuffing;
Binary Beta-PDF Bad-mouthing
G[4] / Functional / Josang-Ismail Ballot-stuffing;
Binary Indistinguishable discounting / Bad-mouthing;
Beta-PDF Sleeper and On-
off
S[6] / Functional + Referral Limited chaining Ballot-stuffing;
Binary / One and aggregation / Bad-mouthing;
Beta-PDF Sleeper and On-
off
Q[28] / Multi-level Functional + Referral / No / Ballot-stuffing;
Multiple Bayesian Bad-mouthing;
Sleeper and On-
Ad Hoc
off; Sybil
Ours / Functional + Referral / No / Ballot-stuffing;
Multi-level Multiple Dirichlet-PDF Bad-mouthing;
Sleeper and On-
off; Conflicting
behavior
May 21, 2012 Trust Networks: T. K. Prasad 131
112. Research Challenges
(What-Why-How of trust?)
HARD PROBLEMS
May 21, 2012 Trust Networks: T. K. Prasad 132
113. Generic Directions
• Finding online substitutes for traditional cues
to derive measures of trust.
• Creating efficient and secure systems for
managing and deriving trust, in order to
support decision making.
Josang et al, 2007
May 21, 2012 Trust Networks: T. K. Prasad 133
114. Robustness Issue
You can fool some of the
people all of the time, and all
of the people some of the
time, but you cannot fool all
of the people all of the time.
Abraham Lincoln,
16th president of US (1809 - 1865)
May 21, 2012 Trust Networks: T. K. Prasad 134
115. Trust : Social Networks vs
Machine Networks
• In social networks such as Facebook, trust is
often subjective, while in machine networks
and social networks such as Twitter, trust can
be given an objective basis and approximated
by trustworthiness.
• Reputation is the perception that an agent
creates through past actions about its
intentions and norms.
– Reputation can be a basis for trust.
May 21, 2012 Trust Networks: T. K. Prasad 135
117. Our Research
Abstract trustworthiness of sensors and
observations to perceptions to obtain actionable
situation awareness!
Web
T
observe perceive
T T
“real-world”
May 21, 2012 Trust Networks: T. K. Prasad 138
118. Trust
Strengthened Trust
May 21, 2012 Trust Networks: T. K. Prasad 139
119. Concrete Application
• Applied Beta-pdf to Mesowest Weather Data
– Used quality flags (OK, CAUTION, SUSPECT)
associated with observations from a sensor
station over time to derive reputation of a sensor
and trustworthiness of a perceptual theory that
explains the observation.
– Perception cycle used data from ~800 stations,
collected for a blizzard during 4/1-6/03.
May 21, 2012 Trust Networks: T. K. Prasad 140
120. Concrete Application
• Perception Cycle
– http://harp.cs.wright.edu/perception/
• Trusted Perception Cycle
– http://www.youtube.com/watch?v=lTxzghCjGgU
May 21, 2012 Trust Networks: T. K. Prasad 141
121. Mean of beta pdf vs. Time (for stnID = SBE)
1.2
1
0.8
Mean Beta
0.6
Value
0.4
0.2
0
3/31/2003 0:00 4/1/2003 0:00 4/2/2003 0:00 4/3/2003 0:00 4/4/2003 0:00 4/5/2003 0:00 4/6/2003 0:00 4/7/2003 0:00
Time
May 21, 2012 Trust Networks: T. K. Prasad 142
122. Research Issues
• Outlier Detection
– Homogeneous Networks
• Statistical Techniques
– Heterogeneous Networks (sensor + social)
• Domain Models
• Distinguishing between abnormal phenomenon
(observation), malfunction (of a sensor), and
compromised behavior (of a sensor)
– Abnormal situations
– Faulty behaviors
– Malicious attacks Ganeriwal et al, 2008
May 21, 2012 Trust Networks: T. K. Prasad 143
124. Our Research
• Study semantic issues relevant to trust
• Proposed model of trust/trust metrics to
formalize indirect trust
May 21, 2012 Trust Networks: T. K. Prasad 145
125. Quote
• Guha et al:
While continuous-valued trusts are
mathematically clean, from the standpoint
of usability, most real-world systems will
in fact use discrete values at which one
user can rate another.
• E.g., Epinions, Ebay, Amazon, Facebook, etc all
use small sets for (dis)trust/rating values.
May 21, 2012 Trust Networks: T. K. Prasad 147
126. Our Approach
Trust formalized in terms of partial orders
(with emphasis on relative magnitude)
Local but realistic semantics
Distinguishes functional and referral trust
Distinguishes direct and inferred trust
Direct trust overrides conflicting inferred trust
Represents ambiguity explicitly
Thirunarayan et al , 2009
May 21, 2012 Trust Networks: T. K. Prasad
127. Formalizing the Framework
• Given a trust network (Nodes AN, Edges RL U
PFL U NFL with Trust Scopes TSF, Local
Orderings ⪯ANxAN), specify when a source can
trust, distrust, or be ambiguous about a
target, reflecting local semantics of:
• Functional and referral trust links
• Direct and inferred trust
• Locality
May 21, 2012 Trust Networks: T. K. Prasad 149
128. (In recommendations)
(For capacity to act)
(For lack of
capacity to act)
May 21, 2012 Trust Networks: T. K. Prasad 150
131. Similarly for Evidence in support of Negative Functional Trust.
May 21, 2012 Trust Networks: T. K. Prasad 153
132. Benefits of Formal Analysis
• Enables detecting and avoiding
unintended consequences.
– An earlier formalization gave priority to “certain“
conclusion from less trustworthy source over
“ambiguous“ conclusion from more trustworthy
source.
The whole problem with the world is
that fools and fanatics are always so
certain of themselves, but wiser people
so full of doubts. — Betrand Russell
May 21, 2012 Trust Networks: T. K. Prasad 154
133. Practical Issues
• Refinement of numeric ratings using
reviews in product rating networks
– Relevance : Separate ratings of vendor or about
extraneous features from ratings of product
• E.g., Issues about Amazon’s policies
• E.g., Publishing under multiple titles (Paul Davies’ “The Goldilock’s
Enigma” vs. “Cosmic Jackpot”)
– Polarity/Degree of support: Check consistency
between rating and review using sentiment
analysis; amplify hidden sentiments
• E.g., rate a phone as 1-star because it is the best
May 21, 2012 Trust Networks: T. K. Prasad 155
134. Research Issues
• Determination of trust / influence from
social networks
– Text analytics on communication
– Analysis of network topology
• E.g., follower relationship, friend relationship, etc.
• Determination of untrustworthy and
anti-social elements in social networks
• HOLY GRAIL: Direct Semantics in favor of
Indirect Translations
May 21, 2012 Trust Networks: T. K. Prasad 157
135. Research Issues
• Improving Security : Robustness to Attack
– How to exploit different trust processes to detect
and recover from attacks?
• Bad mouthing attack
• Ballot stuffing attack
• Sleeper attack
– Temporal trust discounting proportional to trust value
– Using policy-based process to ward-off attack using
reputation-based process
• Sybil attack
• Newcomer attack
May 21, 2012 Trust Networks: T. K. Prasad 160
136. Research Issues
• Intelligent integration of mobile sensor and
social data for situational awareness
– To exploit complementary and corroborative
evidence provided by them
– To obtain qualitative and quantitative context
– To improve robustness and completeness
– To incorporate socio-cultural, linguistic and
behavioral knowledge as part of ontologies to
improve semantic processing and analysis of data
May 21, 2012 Trust Networks: T. K. Prasad 161
137. Complementary and Corroborative
Information
Sensors observe
slow moving
traffic
Complementary
information
from social
networks
May 21, 2012 Trust Networks: T. K. Prasad 162
138. Corroborative Evidence
Evidence for
reported
observations
May 21, 2012 Trust Networks: T. K. Prasad 163
140. Research Issues
• Linguistic clues that betray
trustworthiness
• Experiments for gauging interpersonal
trust in real world situations
– *Techniques and tools to detect and amplify
useful signals in Self to more accurately predict
trust and trustworthiness in Others
*IARPA-TRUST program
May 21, 2012 Trust Networks: T. K. Prasad 165
141. Research Issues
• Other clues for gleaning trustworthiness
– Face (in photo) can effect perceived
trustworthiness and decision making
–Trust-inducing features of e-commerce
sites can impact buyers
– Personal traits: religious beliefs, age,
gullibility, benevolence, etc
– Nature of dyadic relationship
May 21, 2012 Trust Networks: T. K. Prasad 166
142. Research Issues
• Study of cross-cultural differences in
trustworthiness qualities and trust thresholds
to better understand
– Influence
• What aspects improve influence?
– Manipulation
• What aspects flag manipulation?
May 21, 2012 Trust Networks: T. K. Prasad 167
143. Collaborative Systems :
Grid and P2P Computing
May 21, 2012 Trust Networks: T. K. Prasad 168
144. Research Issues
• Trust-aware resource management and
scheduling
– Clients specify resource
preferences/requirements/constraints
• Trust models for P2P systems
– To detect bad domains
– To detect bogus recommendations and attacks
Azzedin and Maheshwaran, 2002-2003
Azzedin and Ridha, 2010
Bessis et al, 2011
May 21, 2012 Trust Networks: T. K. Prasad 169
145. Bayesian Trust Management Framework :
Multi-level Trust Metric
Illustrating a General Approach
Quercia et al 2006
Josang and Haller 2007
May 21, 2012 Trust Networks: T. K. Prasad Thirunarayan et al 2012 170
146. Outline
• Motivation : Multi-level trust management
• Mathematical Foundation: Dirichlet
Distribution
• Implementation and Behavior Details:
– Local Trust Data Structures
– Trust Formation
– Bayesian Trust Evolution
• Analysis of Robustness to Attacks: Security
• Evaluation: Example trace vs. experiment
May 21, 2012 Trust Networks: T. K. Prasad 171
147. Motivation
• Uses K-level discrete trust metric
– E.g., Amazon’s 5-star trust metric can be
interpreted as signifying (very untrustworthy,
untrustworthy, neutral, trustworthy, very
trustworthy) or (very dissatisfied, dissatisfied,
neutral, satisfied, very satisfied).
May 21, 2012 Trust Networks: T. K. Prasad 172
148. Approach
• Multi-level trust management approach
formalizes a distributed, robust,
lightweight, computational trust that
takes into account context, subjectivity,
and time.
• Applies Dirichlet distribution, a
generalization of Beta-distribution.
May 21, 2012 Trust Networks: T. K. Prasad 173
150. K-level Trust Metric
• K-level trust probability vector:
x = (x1, . . ., xK)
where (x1 + . . . + xK = 1).
• Example: If a 5-star rating system has 50 people
giving 5-stars, 20 people giving 4-stars, 5 people
giving 3-stars, 5 people giving 2-stars, and 20 people
giving 1-star, then the 5-level trust metric probability
vector is (0.5,0.2,0.05,0.05,0.2).
May 21, 2012 Trust Networks: T. K. Prasad 175
151. Trust and Experience
• Experience is a realization of latent trust and
helps predicting trust.
• Probability of an experience-level sequence,
with a1 - 1 counts of level 1 experience, …, aK - 1
counts of level K experience is:
* ( (a1 +…+ aK – K) ! / (a1-1 ! *…* aK-1 !) )
May 21, 2012 Trust Networks: T. K. Prasad 176
152. Dirichlet Distribution
• The Dirichlet distribution is the probability
density function for x = (x1, . . ., xK) given
(a1,…,aK):
May 21, 2012 Trust Networks: T. K. Prasad 177
153. Why use Dirichlet Distribution?
• If the prior distribution of x is uniform, then
the Dirichlet family of distribution shown
below gives posterior distribution of x after
ai-1 occurrences of level i experience with
probability xi, for each i in [1, K]:
May 21, 2012 Trust Networks: T. K. Prasad 178
154. Why use Dirichlet Distribution?
• Dirichlet distribution is a conjugate prior for
multinomial distribution.
• Consequence:
– Estimated distribution updated for a new experience
at level i, by just incrementing ai parameter.
– In contrast: if prior distribution is different from
Dirichlet, then it is conceptually hard to comprehend
and computationally inefficient to compute posterior
distribution, in general.
– Icing on the cake: Uniform distribution (signifying
ignorance) is Dirichlet!
May 21, 2012 Trust Networks: T. K. Prasad 179
155. Dirichlet distribution is a conjugate prior
for multinomial distribution.
May 21, 2012 Trust Networks: T. K. Prasad 180
156. Why use Dirichlet Distribution?
• Convenient Abstraction
–Abstraction of K-level Dirichlet
distribution by combining different
levels still yields Dirichlet distribution
with the corresponding parameters
merged.
• Conceptually and computationally
pleasing property
May 21, 2012 Trust Networks: T. K. Prasad 181
158. Dynamic Trustworthiness
• Best estimate of trust for Dir(a1,...,aK) (gleaned
from (ai-1) experiences at level i, for all i in [1,K])
is the mean vector (a1/a0,…,aK/a0), and the
associated confidence is the variance vector.
May 21, 2012 Trust Networks: T. K. Prasad 183
160. Local Data Structures
• To store relevant information to compute
direct (functional) and indirect (referral) trust.
• Each node maintains locally, for each peer and
each context, four vectors of length K.
May 21, 2012 Trust Networks: T. K. Prasad 185
161. Local Data Structures
• Direct Trust Vector: Peers X Contexts X Peers -> Probability-Vector-K
• dtv(px,c,py) = (d1,d2,…,dK)
• Direct Experience Matrix: Peers X Contexts X Peers -> Count-Vector-K
• dem(px,c,py) = (ec1,…,ecK)
May 21, 2012 Trust Networks: T. K. Prasad 186
162. Local Data Structures
• Recommended Trust Vector: Peers X Contexts X Peers -> Probability-
Vector-K
• rtv(px,c,py) = (r1,r2,…,rK)
• Sent Recommendation Matrix: Peers X Contexts X Peers -> Count-Vector-K
• srm(px,c,py) = (sr1,…,srK)
May 21, 2012 Trust Networks: T. K. Prasad 187
163. Local Data Structures
• Initialization: To reflect complete
ignorance via uniform distribution, we
set the probability vectors dtv and rtv to
(1/K,…,1/K), and the elements of the
count vector dem and srm to (0,…,0).
• These are Dirichlet distributed in the
limiting case where ai’s are 1.
May 21, 2012 Trust Networks: T. K. Prasad 188
164. Trust Formation
• Overall trust vector is weighted combination
of direct trust vector and recommended trust
vector.
• Weights determined using
– Objective confidence values using variance
(deviation from the mean)
– Subjective relative preference for direct
experience over recommendations
• Dependence on recommended trust yet to be explored
May 21, 2012 Trust Networks: T. K. Prasad 189
165. Trust Decision
• Assuming that trust-level scale is linear, the
trust distribution vector (d1,d2,…,dK) can be
mapped to the closed interval [0,1], or to
consolidated trust level, in order to act.
• Trust threshold should be determined based
on the context and risk tolerance / disposition
/ propensity to trust.
May 21, 2012 Trust Networks: T. K. Prasad 190
166. Trust Evolution
• Direct (recommended) trust vectors are updated
for a new experience (recommendation).
• Key Idea: Dirichlet distribution is the conjugate
prior of the multinomial distribution. So it is
adequate to maintain counts of direct experience
and sent recommendations, to best estimate
direct trust and recommended trust vectors
respectively.
May 21, 2012 Trust Networks: T. K. Prasad 191
167. Trust Evolution
• Simple Scheme (Direct Trust)
For a new experience at level i,
dem(px,x,py) = (ec1,…,ecK) becomes
demnew(px,x,py) = (ec1,…, eci+1,…,ecK)
and dtv(px,c,py) becomes
dtvnew(px,c,py) = (d1,d2,…,dK)
where di = eci+1 / (ec1 + … + eck+1) and
dj = ecj / (ec1 + … + eck+1)
for each j in [1,K] and j =/= i.
May 21, 2012 Trust Networks: T. K. Prasad 192
168. Trust Evolution
• Robust Scheme
To incorporate differential aging of experience
counts as a function of their level (and to
incorporate “long term memory for low-level
experience and short term memory for high-
level experience”), we use a decay vector
(l1,…,lK), where 1 >= l1 >= … >= lK > 0, that
modifies update rule as:
May 21, 2012 Trust Networks: T. K. Prasad 193
169. Trust Evolution
• Robust Scheme (Direct Trust)
For a new experience at level i,
dem(px,x,py) = (ec1,…,ecK) becomes
demnew(px,x,py) = (ec1,…, eci + 1,…,ecK).
For every clock tick (with context-based delay),
dem(px,x,py) = (ec1,…,ecK) becomes
demnew(px,x,py) = (l1*ec1,…, lK*ecK)
May 21, 2012 Trust Networks: T. K. Prasad 194
170. Trust Evolution
• Robust Scheme (Direct Trust)
For every clock unit and new experience,
dtv(px,c,py) becomes
dtvnew(px,c,py) = (d1,d2,…,dK)
where di = eci / (ec1 + … + eck)
for each i in [1,K].
• Subtlety: Experience counts should saturate at
1 rather than diminish to 0 with time. (See code)
May 21, 2012 Trust Networks: T. K. Prasad 195
171. Trust Evolution Illustrated
Final Trust Distribution Final Trust Distribution
Experience Sequence
(Simple Scheme) (Robust Scheme)
[1,1,1] (0.57,0.14,0.14,0.14)
(0.55,0.15,0.15,0.15)
[1,4,1,4] (0.375,0.125,0.125,0.375)
(0.42,0.14,0.14,0.29)
[1,1,4,4,4,4,1,1] (0.42,0.08,0.08,0.42)
(0.5,0.1,0.1,0.3)
[1,1,4,4,4,4,1,1,1] (0.53,0.07,0.07,0.33)
(0.64,0.1,0.1,0.17)
[2,3,2,3] (0.125,0.375,0.375,0.125)
(0.16,0.4,0.3,0.14)
May 21, 2012 Trust Networks: T. K. Prasad 196
172. Trust Evolution Illustrated
Experience Trust Distribution Trace Trust Distribution Trace Beta-pdf
Sequence Value (Simple Scheme) (Robust Scheme) (cf. n =2)
(0.25,0.25,0.25,0.25) (0.25,0.25,0.25,0.25) 0.5
1 (0.4,0.2,0.2,0.2) (0.4,0.2,0.2,0.2) 0.33
1 (0.5,0.17,0.17,0.17) (0.53,0.165,0.155,0.15) 0.25
1 (0.57,0.14,0.14,0.14) (0.55,0.15,0.15,0.15) 0.2
n (0.5,0.125,0.125,0.25) (0.5,0.13,0.12,0.25) 0.33
n (0.44,0.11,0.11,0.33) (0.46,0.135,0.135,0.27) 0.43
n (0.4,0.1,0.1,0.4) (0.42,0.12,0.11,0.35) 0.5
n (0.36,0.1,0.1,0.45) (0.37,0.12,0.12,0.38) 0.55
1 (0.42,0.08,0.08,0.41) (0.47,0.11,0.11,0.31) 0.5
1 (0.46,0.08,0.08,0.38) (0.53,0.11,0.11,0.24) 0.45
1 (0.5,0.07,0.07,0.35) (0.6,0.1,0.1,0.2) 0.41
1 (0.53,0.07,0.07,0.33) (0.65,0.1,0.1,0.14) 0.38
n (0.5,0.0625,0.0625,0.375) (0.6,0.1,0.1,0.2) 0.43
1 (0.53,0.06,0.06,0.35) (0.64,0.1,0.1,0.17) 0.4
May 21, 2012 Trust Networks: T. K. Prasad 197
176. Salient Properties
• Symmetry
– Simple Scheme is symmetric w.r.t.
trust/experience levels while Robust Scheme is
somewhat asymmetric because of non-uniform
decay.
– Experience levels are “preserved” in that
extreme/controvertial behavior (credulous
interpretation) is treated differently from
ignorance (skeptical interpretation).
May 21, 2012 Trust Networks: T. K. Prasad 201
177. Salient Properties
• Effect of Order of Experience
– Simple Scheme is sensitive to the counts of
various experience levels, but not to the order of
experience.
– Robust Scheme is sensitive to the order of
experience.
May 21, 2012 Trust Networks: T. K. Prasad 202
178. Salient Properties
• Differential Aging of experience levels
• It exhibits limited and selective
memory.
–It retains low-level experiences
much longer than high-level
experiences.
»Parameters: Decay rate and
saturation
May 21, 2012 Trust Networks: T. K. Prasad 203
179. Related Work on Multi-level Trust
with Applications
The described approach is similar to Dirichlet Reputation
System [Josang-Haller, 2007].
Applications:
• Browser toolbar for clients to see the user ratings and
for users to provide ratings (critical surfer model)
[Josang-Haller, 2007]
• Evaluating partners in Collaborative Environments
[Yang and Cemerlic, 2009]
• Formalizing Multi-Dimensional Contracts [Reece, et al,
2007]
• In Collaborative Intrusion Detection System [Fung et al,
2011 ]
May 21, 2012 Trust Networks: T. K. Prasad 204
180. Conclusion
• Provided simple examples of trust (Why?)
• Explained salient features of trust (What?)
• Showed examples of gleaning trustworthiness
(How?)
• Touched upon research challenges in the
context of
• Sensor Networks
• Social Networks
• Interpersonal Networks
• Collaborative Environments
May 21, 2012 Trust Networks: T. K. Prasad 206
181. Holy Grail
for Automatic Trust Computation
Develop expressive trust
networks that can be assigned
objective semantics.
May 21, 2012 Trust Networks: T. K. Prasad 207