This document discusses privacy-enhanced personalization by balancing user privacy and personalized experiences online. It summarizes research showing that contextual explanations of privacy practices and personalization benefits lead users to disclose more information and have more positive perceptions of websites. While privacy-enhancing technologies can help, interaction design is also important. User trust, understanding, and control play a key role in finding the right balance between privacy and personalization. More research is still needed on factors like website reputation and the visibility of privacy information.
Social Networks in Health Care - Talk at ICSE 2010James Williams
A talk given at the Software Engineering for Health Care workshop at ICSE 2010 (Cape Town). Reviews privacy and security issues for social networking in the health care domain, covers some existing work, and points out future directions.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’ preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the results.
This document discusses social media and its use in enterprises. It covers topics like defining social media, how enterprises use it, related ethics and impacts on privacy and intellectual property. Case studies of companies like Facebook are discussed. The document also covers managing ethical issues around information systems, including principles of privacy, property rights, accountability and quality of life. Fair information practices and their application to privacy laws are summarized.
Examining the Effect of Individual Differences and Concerns Related toAli Zeinoddini Meymand
This document summarizes a research paper that examines the effect of individual differences and privacy concerns on consumers' perceptions of using information technology. It discusses how factors like self-esteem, alienation, computer anxiety, and privacy concerns around data collection and use can influence attitudes and intentions to use IT. The document reviews relevant literature on privacy concepts, comprehensive security programs for e-commerce, and a proposed research model examining the relationships between individual characteristics, privacy concerns, attitudes, and behavioral intentions.
Research Using Behavioral Big Data: A Tour and Why Mechanical Engineers Shoul...Galit Shmueli
Keynote address by Galit Shmueli at 2016 Israeli Conference on Mechanical Engineering (ICME), Technion, Israel (Nov 23, 2016). http://icme2016.net.technion.ac.il/
RELATIONS BETWEEN LEARNING ANALYTICS AND DATA PRIVACY IN MOOCsMalinka Ivanova
This document discusses relations between learning analytics and data privacy in MOOCs. It aims to summarize current projects, practices, and research on using MOOC data for learning analytics while ensuring data privacy. It outlines that MOOC platforms collect personal data from users for various purposes and share it with third parties. A proposed CIC model aims to classify data and give users choices regarding data sharing and movement between classifications. The conclusion is that more work is needed to develop complete data privacy policies and tools, as current analytics utilize data with minimal privacy protections.
To develop a model of students’ data privacy in eLearning that protects student privacy while supporting educational goals. The model considers what types of data educators need to manage learning, what data students are willing to share, and technical privacy measures for educational software. A survey asked students about sharing data via educational software, what data educators require, the role of intelligent technologies, and student control over data sharing. The proposed model balances student and educator needs through techniques like pseudonymity, anonymity, and student-selected data sharing within secure systems.
Social Networks in Health Care - Talk at ICSE 2010James Williams
A talk given at the Software Engineering for Health Care workshop at ICSE 2010 (Cape Town). Reviews privacy and security issues for social networking in the health care domain, covers some existing work, and points out future directions.
Integration of Bayesian Theory and Association Rule Mining in Predicting User...Editor IJCATR
Bayesian theory and association rule mining methods are artificial intelligence techniques that have been used in various computing fields, especially in machine learning. Internet has been considered as an easy ground for vices like radicalization because of its diverse nature and ease of information access. These vices could be managed using recommender systems methods which are used to deliver users’ preference data based on their previous interests and in relation with the community around the user. The recommender systems are divided into two broad categories, i.e. collaborative systems which considers users which share the same preferences as the user in question and content-based recommender systems tends to recommend websites similar to those already liked by the user. Recent research and information from security organs indicate that, online radicalization has been growing at an alarming rate. The paper reviews in depth what has been carried out in recommender systems and looks at how these methods could be combined to from a strong system to monitor and manage online menace as a result of radicalization. The relationship between different websites and the trend from continuous access of these websites forms the basis for probabilistic reasoning in understanding the users’ behavior. Association rule mining method has been widely used in recommender systems in profiling and generating users’ preferences. To add probabilistic reasoning considering internet magnitude and more so in social media, Bayesian theory is incorporated. Combination of this two techniques provides better analysis of the results thereby adding reliability and knowledge to the results.
This document discusses social media and its use in enterprises. It covers topics like defining social media, how enterprises use it, related ethics and impacts on privacy and intellectual property. Case studies of companies like Facebook are discussed. The document also covers managing ethical issues around information systems, including principles of privacy, property rights, accountability and quality of life. Fair information practices and their application to privacy laws are summarized.
Examining the Effect of Individual Differences and Concerns Related toAli Zeinoddini Meymand
This document summarizes a research paper that examines the effect of individual differences and privacy concerns on consumers' perceptions of using information technology. It discusses how factors like self-esteem, alienation, computer anxiety, and privacy concerns around data collection and use can influence attitudes and intentions to use IT. The document reviews relevant literature on privacy concepts, comprehensive security programs for e-commerce, and a proposed research model examining the relationships between individual characteristics, privacy concerns, attitudes, and behavioral intentions.
Research Using Behavioral Big Data: A Tour and Why Mechanical Engineers Shoul...Galit Shmueli
Keynote address by Galit Shmueli at 2016 Israeli Conference on Mechanical Engineering (ICME), Technion, Israel (Nov 23, 2016). http://icme2016.net.technion.ac.il/
RELATIONS BETWEEN LEARNING ANALYTICS AND DATA PRIVACY IN MOOCsMalinka Ivanova
This document discusses relations between learning analytics and data privacy in MOOCs. It aims to summarize current projects, practices, and research on using MOOC data for learning analytics while ensuring data privacy. It outlines that MOOC platforms collect personal data from users for various purposes and share it with third parties. A proposed CIC model aims to classify data and give users choices regarding data sharing and movement between classifications. The conclusion is that more work is needed to develop complete data privacy policies and tools, as current analytics utilize data with minimal privacy protections.
To develop a model of students’ data privacy in eLearning that protects student privacy while supporting educational goals. The model considers what types of data educators need to manage learning, what data students are willing to share, and technical privacy measures for educational software. A survey asked students about sharing data via educational software, what data educators require, the role of intelligent technologies, and student control over data sharing. The proposed model balances student and educator needs through techniques like pseudonymity, anonymity, and student-selected data sharing within secure systems.
White Paper: The 2015 State of Consumer Privacy & PersonalizationGigya
This year’s survey results illustrate increased growth and intensity in consumer desire for data privacy and personalized user experiences. While social login usage continues to skyrocket, consumers are also showing a marked interest in next-generation authentication methods, including payment providers and biometrics, indicating a clear evolution of the concept of digital identity: Identity 3.0.
2016 Drupal Camp Asheville: Web Personalization and Marketing Automation with...Jason Want
This document provides an introduction to marketing automation and web personalization with Drupal. It discusses what web personalization and marketing automation are, their benefits, and available solutions. It then outlines how these concepts can be implemented with Drupal, including available modules for personalization and integrations for marketing automation platforms. Specific modules and features discussed include Personalization, Acquia Lift, Pardot, and Eloqua integrations.
Customer experience - how your brand lives or diesTom Voirol
This document discusses improving customer experience. It begins by introducing the concept of customer experience and how experiences are shaped by emotions and shared stories. It then tells the story of a musician, Dave Carroll, who had bad experiences with United Airlines that he shared online. This gained widespread attention. The document then discusses the Kano model for understanding customer needs and delighting customers. It provides examples of basic, performance-based and excitement-generating customer experiences. The rest of the document outlines five steps to improve customer experience: 1) Know your customers 2) Find all customer touchpoints 3) Discover what can be improved 4) Design the new experience 5) Prototype, test and repeat.
Enhancing Information Retrieval by Personalization Techniquesveningstonk
This document outlines the research modules proposed for a PhD thesis focused on enhancing information retrieval through personalization techniques. The research will include four modules: 1) enhancing retrieval using term association graph representation, 2) integrating document and user topic models for personalization, 3) using genetic algorithms for document re-ranking, and 4) employing ant colony optimization for query reformulation. Module 1 will represent documents as a term graph and use the graph to re-rank documents based on term associations. The methodology for Module 1 includes preprocessing, frequent itemset mining to construct the term graph, and approaches for ranking documents based on semantic associations in the graph.
Turning Big Data into More Effective Customer ExperiencesNG DATA
Discover how you can improve customer experiences and increase profitability for Telecoms.
To learn more about NGDATA or Lily Enterprise 3.0, please visit ngdata.com
"Using Data Science to Design Effective Precision Preventative Behavioral Med...Hyper Wellbeing
"Using Data Science to Design Effective Precision Preventative Behavioral Medicine" - Ryan Quan (Data Scientist, Omada Health)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
Ethics of personalized information filteringAnsgar Koene
This document discusses the ethics of personalized information filtering. It notes that while personalized filtering is a natural evolution, it raises privacy concerns due to user profiling and a lack of transparency. This can allow for potential manipulation of users through covert influencing of their choices and views. The document calls for responsible research and innovation to address these issues, through techniques like black-box testing of recommender systems, detecting recommendation bias, and developing guidelines for ethical use while protecting user privacy and freedom of access to information.
The document discusses several topics related to ethics and privacy when dealing with information technology:
1. It introduces four common ethical frameworks used to evaluate decisions: utilitarian, rights-based, fairness-based, and common good approaches.
2. It outlines some fundamental tenets of ethics like responsibility, accountability, and liability that are important in a corporate environment.
3. It identifies four general categories of ethical issues related to IT: privacy, accuracy, property, and accessibility. It provides examples of issues that fall under each category.
4. It focuses specifically on privacy issues, outlining concerns around electronic surveillance, personal information in databases, information shared online, and international differences in privacy laws and standards.
This document discusses information privacy and its technical, organizational, and social implications. It begins by defining information privacy and the relationship between data collection, technology, public expectations of privacy, and legal issues. It then covers topics like personally identifiable information, the types of data collected online, and technical tools and devices related to privacy. The document also addresses the costs of information privacy for governments, companies, and consumers. It discusses perspectives on privacy from different generations and countries. Finally, it covers organizational privacy policies and standards, as well as some high-profile data breach cases and the importance of information security.
This paper was presented at the 'Towards a Magna Carta for Data' workshop at the RDS in Dublin, Sept 17th. It discusses how considerations of the ethics of big data consist of much more than the issues of privacy and security that it often gets boiled down to, and argues that the various ethical issues related to big data are multidimensional and contested; vary in nature across domains, and which ethical philosophy is adopted matters to the deliberation over data rights.
Marketers know they need complete data to deliver a great customer experience, but few actually have built the data they need. Maybe they don't know how, but more likely they just are spending their time on other things that seem more important. This presentation shows the great things they could do if they had better data in place, in the hopes of convincing them to give data a higher priority. It has kittens too.
#1NWebinar: GDPR and Privacy Best Practices for Digital MarketersOne North
One North’s Managing Director of Technology Ryan Horner and legal process and technology consultant Bob Beach share details on how the EU’s General Data Protection Regulation (GDPR) could impact digital assets.
This webinar is designed to educate digital marketers, share actionable examples, and provide an overview of how One North can help clients ensure their digital properties are in compliance with the regulation and execute on those efforts. Beyond GDPR compliance, the session will also highlight important information for marketers as data privacy continues to become a critical and strategic component of digital.
Access the recording: https://youtu.be/ruQpN70LGt0
Lee Rainie, Director of Internet, Science, and Technology research at the Pew Research Center, presented this material on December 12, 2016 to a working group at the National Academy of Sciences. The group is exploring how to think about creating an academic discipline around "data science."
This document outlines the course roadmap for a data analytics course. It includes 12 topics covered over 15 weeks, with flexibility weeks built in. The topics include data exploration and visualization, predictive analytics, research design and experimentation, and data communication. Workshops are included to provide hands-on learning opportunities. The learning objectives focus on key principles of data ethics like ethical decision making, technical approaches to prevent issues, and risk management for data ethics.
The presentation is all about the issues in professional ethics. This talks about the failures of ethics in Information Technology. Sliding thru the powerpoint gives you a hint what are the ethical and social issues in information systems
Making ‘Big Data’ Your Ally – Using data analytics to improve compliance, due...emermell
This document summarizes a presentation on using data analytics for compliance, due diligence, and investigations. The presentation features four speakers: Raul Saccani of Deloitte, Dave Stewart of SAS Institute, John Walsh of SightSpan, and John Walsh of SAS Institute. It discusses challenges related to big data including volume, variety, and velocity of data. It provides examples of how financial institutions have used analytics for anti-money laundering model tuning and illicit network analysis. It also outlines the analytics lifecycle and considerations for adopting a proactive analytics strategy.
A Lifecycle Approach to Information PrivacyMicah Altman
The document discusses challenges in privacy across the lifecycle of data from collection to dissemination and proposes taking a lifecycle approach. It analyzes how concepts like differential privacy could address issues raised at different stages and questions that approach generates regarding legal and technical issues. The goal is to advance interdisciplinary research at the intersection of law, social science, public policy, data collection methods, data management, statistics, and computer science.
This document discusses several ethical, social, and political issues raised by information systems. It addresses principles of responsibility, accountability, and liability regarding data use and privacy protection. Contemporary technologies like data mining and predictive modeling pose challenges to privacy and intellectual property. Laws and policies are still developing to address these issues.
Blockchain & GDPR vs. Facebook - how blockchain protects personal data and en...Christian Lange
The presentation shows how Opiria market research platform utilizes blockchain technology to protect consumers´ data privacy and enable fair trading of personal data between users and companies
The document discusses ethical and social issues related to information systems in business. It covers topics like ethics in information system design and use, identifying and addressing ethical issues, examples of organizations violating ethics, and the relationship between society, information systems, and business. The document also provides examples of how businesses can address ethical concerns through codes of conduct, clear policies, transparency, and decision-making frameworks. Additionally, it discusses social issues such as privacy, responsibility, isolation, and how businesses can contribute to sustainability and social causes through responsible use of information systems.
White Paper: The 2015 State of Consumer Privacy & PersonalizationGigya
This year’s survey results illustrate increased growth and intensity in consumer desire for data privacy and personalized user experiences. While social login usage continues to skyrocket, consumers are also showing a marked interest in next-generation authentication methods, including payment providers and biometrics, indicating a clear evolution of the concept of digital identity: Identity 3.0.
2016 Drupal Camp Asheville: Web Personalization and Marketing Automation with...Jason Want
This document provides an introduction to marketing automation and web personalization with Drupal. It discusses what web personalization and marketing automation are, their benefits, and available solutions. It then outlines how these concepts can be implemented with Drupal, including available modules for personalization and integrations for marketing automation platforms. Specific modules and features discussed include Personalization, Acquia Lift, Pardot, and Eloqua integrations.
Customer experience - how your brand lives or diesTom Voirol
This document discusses improving customer experience. It begins by introducing the concept of customer experience and how experiences are shaped by emotions and shared stories. It then tells the story of a musician, Dave Carroll, who had bad experiences with United Airlines that he shared online. This gained widespread attention. The document then discusses the Kano model for understanding customer needs and delighting customers. It provides examples of basic, performance-based and excitement-generating customer experiences. The rest of the document outlines five steps to improve customer experience: 1) Know your customers 2) Find all customer touchpoints 3) Discover what can be improved 4) Design the new experience 5) Prototype, test and repeat.
Enhancing Information Retrieval by Personalization Techniquesveningstonk
This document outlines the research modules proposed for a PhD thesis focused on enhancing information retrieval through personalization techniques. The research will include four modules: 1) enhancing retrieval using term association graph representation, 2) integrating document and user topic models for personalization, 3) using genetic algorithms for document re-ranking, and 4) employing ant colony optimization for query reformulation. Module 1 will represent documents as a term graph and use the graph to re-rank documents based on term associations. The methodology for Module 1 includes preprocessing, frequent itemset mining to construct the term graph, and approaches for ranking documents based on semantic associations in the graph.
Turning Big Data into More Effective Customer ExperiencesNG DATA
Discover how you can improve customer experiences and increase profitability for Telecoms.
To learn more about NGDATA or Lily Enterprise 3.0, please visit ngdata.com
"Using Data Science to Design Effective Precision Preventative Behavioral Med...Hyper Wellbeing
"Using Data Science to Design Effective Precision Preventative Behavioral Medicine" - Ryan Quan (Data Scientist, Omada Health)
Delivered at the inaugural Hyper Wellbeing Summit, 14th November 2016, Mountain View, California.
For more information including details of subsequent events, please visit http://hyperwellbeing.com
The summit was created to foster a community around an emerging industry - Wellness as a Service (WaaS). Consumer technologies, in particular wearables and mobile, are powering a consumer revolution. A revolution to turn health and wellness into platform delivered services. A revolution enabling consumer data-driven disease risk reduction. A revolution extending health care past sick care towards consumer-led lifelong health, wellness and lifestyle optimization.
WaaS newsletter sign-up http://eepurl.com/b71fdr
@hyperwellbeing
Ethics of personalized information filteringAnsgar Koene
This document discusses the ethics of personalized information filtering. It notes that while personalized filtering is a natural evolution, it raises privacy concerns due to user profiling and a lack of transparency. This can allow for potential manipulation of users through covert influencing of their choices and views. The document calls for responsible research and innovation to address these issues, through techniques like black-box testing of recommender systems, detecting recommendation bias, and developing guidelines for ethical use while protecting user privacy and freedom of access to information.
The document discusses several topics related to ethics and privacy when dealing with information technology:
1. It introduces four common ethical frameworks used to evaluate decisions: utilitarian, rights-based, fairness-based, and common good approaches.
2. It outlines some fundamental tenets of ethics like responsibility, accountability, and liability that are important in a corporate environment.
3. It identifies four general categories of ethical issues related to IT: privacy, accuracy, property, and accessibility. It provides examples of issues that fall under each category.
4. It focuses specifically on privacy issues, outlining concerns around electronic surveillance, personal information in databases, information shared online, and international differences in privacy laws and standards.
This document discusses information privacy and its technical, organizational, and social implications. It begins by defining information privacy and the relationship between data collection, technology, public expectations of privacy, and legal issues. It then covers topics like personally identifiable information, the types of data collected online, and technical tools and devices related to privacy. The document also addresses the costs of information privacy for governments, companies, and consumers. It discusses perspectives on privacy from different generations and countries. Finally, it covers organizational privacy policies and standards, as well as some high-profile data breach cases and the importance of information security.
This paper was presented at the 'Towards a Magna Carta for Data' workshop at the RDS in Dublin, Sept 17th. It discusses how considerations of the ethics of big data consist of much more than the issues of privacy and security that it often gets boiled down to, and argues that the various ethical issues related to big data are multidimensional and contested; vary in nature across domains, and which ethical philosophy is adopted matters to the deliberation over data rights.
Marketers know they need complete data to deliver a great customer experience, but few actually have built the data they need. Maybe they don't know how, but more likely they just are spending their time on other things that seem more important. This presentation shows the great things they could do if they had better data in place, in the hopes of convincing them to give data a higher priority. It has kittens too.
#1NWebinar: GDPR and Privacy Best Practices for Digital MarketersOne North
One North’s Managing Director of Technology Ryan Horner and legal process and technology consultant Bob Beach share details on how the EU’s General Data Protection Regulation (GDPR) could impact digital assets.
This webinar is designed to educate digital marketers, share actionable examples, and provide an overview of how One North can help clients ensure their digital properties are in compliance with the regulation and execute on those efforts. Beyond GDPR compliance, the session will also highlight important information for marketers as data privacy continues to become a critical and strategic component of digital.
Access the recording: https://youtu.be/ruQpN70LGt0
Lee Rainie, Director of Internet, Science, and Technology research at the Pew Research Center, presented this material on December 12, 2016 to a working group at the National Academy of Sciences. The group is exploring how to think about creating an academic discipline around "data science."
This document outlines the course roadmap for a data analytics course. It includes 12 topics covered over 15 weeks, with flexibility weeks built in. The topics include data exploration and visualization, predictive analytics, research design and experimentation, and data communication. Workshops are included to provide hands-on learning opportunities. The learning objectives focus on key principles of data ethics like ethical decision making, technical approaches to prevent issues, and risk management for data ethics.
The presentation is all about the issues in professional ethics. This talks about the failures of ethics in Information Technology. Sliding thru the powerpoint gives you a hint what are the ethical and social issues in information systems
Making ‘Big Data’ Your Ally – Using data analytics to improve compliance, due...emermell
This document summarizes a presentation on using data analytics for compliance, due diligence, and investigations. The presentation features four speakers: Raul Saccani of Deloitte, Dave Stewart of SAS Institute, John Walsh of SightSpan, and John Walsh of SAS Institute. It discusses challenges related to big data including volume, variety, and velocity of data. It provides examples of how financial institutions have used analytics for anti-money laundering model tuning and illicit network analysis. It also outlines the analytics lifecycle and considerations for adopting a proactive analytics strategy.
A Lifecycle Approach to Information PrivacyMicah Altman
The document discusses challenges in privacy across the lifecycle of data from collection to dissemination and proposes taking a lifecycle approach. It analyzes how concepts like differential privacy could address issues raised at different stages and questions that approach generates regarding legal and technical issues. The goal is to advance interdisciplinary research at the intersection of law, social science, public policy, data collection methods, data management, statistics, and computer science.
This document discusses several ethical, social, and political issues raised by information systems. It addresses principles of responsibility, accountability, and liability regarding data use and privacy protection. Contemporary technologies like data mining and predictive modeling pose challenges to privacy and intellectual property. Laws and policies are still developing to address these issues.
Blockchain & GDPR vs. Facebook - how blockchain protects personal data and en...Christian Lange
The presentation shows how Opiria market research platform utilizes blockchain technology to protect consumers´ data privacy and enable fair trading of personal data between users and companies
The document discusses ethical and social issues related to information systems in business. It covers topics like ethics in information system design and use, identifying and addressing ethical issues, examples of organizations violating ethics, and the relationship between society, information systems, and business. The document also provides examples of how businesses can address ethical concerns through codes of conduct, clear policies, transparency, and decision-making frameworks. Additionally, it discusses social issues such as privacy, responsibility, isolation, and how businesses can contribute to sustainability and social causes through responsible use of information systems.
This document contains a copyright notice for an educational presentation on information systems prepared by Arianto Muditomo for Perbanas Institute. It states that the presentation materials are for non-commercial educational use only and cannot be altered or used for commercial purposes without written permission. The document lists references used in the presentation and provides an outline of the presentation topics, which include information systems in business, IT strategic planning, business intelligence and decision support, ethics and security, e-business and e-commerce, and knowledge management.
How People Care about their Personal Datatheir Data Released onReleased on So...Kellyton Brito
Content sharing services have become immensely popular on the Web. More than 1 billion people use this kind of services to communicate with friends and exchange all sorts of information. In this new context, privacy guarantees are essential: guarantees about the potential release of data to unintended recipients and the use of user data by the service provider. Although the general public is concerned about privacy questions related to unintended audiences, data usage by service providers is still misunderstood. In order to further explore this level of misunderstanding, this work presents the results of a survey conducted among 900 people with the aim of discovering how people care about the use of their personal data by service providers in terms of social media. From the results, we found that: (i) in general people do not read license terms and do not know very much about service policies, and when presented with these policies people do not agree with them; (ii) a good number of people would support alternative models such as paying for privacy or selling their personal data; and (iii) there are some differences between generations in relation to how they care about their data.
This document discusses data ethics and provides 5 key principles of data ethics for business professionals:
1) Ownership - individuals own their personal data and must provide consent for it to be collected
2) Transparency - individuals have a right to know how their data will be collected, stored, and used
3) Privacy - personal data must be securely stored and protected from unauthorized access
4) Intention - the intention behind collecting data must be considered to avoid potential harm
5) Outcomes - while intentions may be good, data analysis could inadvertently cause disparate impacts
Upholding data ethics helps businesses earn customer trust, which is essential to their success. Failure to do so can damage reputations and result
This document summarizes a webinar on data ethics when designing civil justice interventions. It provides an agenda for the webinar which includes introductions, a discussion of how machines can learn to discriminate with Solon Barocas speaking, and a discussion of digital decision making with Ali Lange speaking. It also includes information about the speakers and a question period. Key topics discussed are how big data can unintentionally reinforce biases and disparities if human oversight is lacking, and the importance of considering data sensitivities, consumer protection laws, and empowering clients when using big data.
Human(e) machine interaction? A reflection on the development of productsIHM'10
1) Product development processes often have gaps in knowledge about and engagement with users. Designers struggle to truly understand users and incorporate their perspectives.
2) There are calls to move beyond a user-centered approach to one of user integration, where users are systematically involved throughout the development process.
3) While usability aims to ensure effectiveness, efficiency and satisfaction, the full user experience encompasses emotional responses and involvement that must also be considered in design.
Hotspot Based Mobile Web Communication and CooperationIHM'10
This document proposes a new approach to mobile web and location-based services (LBS) for communication and collaboration using physical hotspots. It describes 8 basic communication situations involving interactions between actors and servers at both the global and local hotspot level. A case study examines using a bus shelter hotspot for contextual transportation information and social collaboration in the local community. The hotspot would provide services like transportation updates, special requests, ridesharing, and neighborhood information exchange. Future work involves prototypes and studies to evaluate the utility, usability and acceptability of the hotspot approach.
A Comprehensive Guide to DeFi Development Services in 2024Intelisync
DeFi represents a paradigm shift in the financial industry. Instead of relying on traditional, centralized institutions like banks, DeFi leverages blockchain technology to create a decentralized network of financial services. This means that financial transactions can occur directly between parties, without intermediaries, using smart contracts on platforms like Ethereum.
In 2024, we are witnessing an explosion of new DeFi projects and protocols, each pushing the boundaries of what’s possible in finance.
In summary, DeFi in 2024 is not just a trend; it’s a revolution that democratizes finance, enhances security and transparency, and fosters continuous innovation. As we proceed through this presentation, we'll explore the various components and services of DeFi in detail, shedding light on how they are transforming the financial landscape.
At Intelisync, we specialize in providing comprehensive DeFi development services tailored to meet the unique needs of our clients. From smart contract development to dApp creation and security audits, we ensure that your DeFi project is built with innovation, security, and scalability in mind. Trust Intelisync to guide you through the intricate landscape of decentralized finance and unlock the full potential of blockchain technology.
Ready to take your DeFi project to the next level? Partner with Intelisync for expert DeFi development services today!
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
leewayhertz.com-AI in predictive maintenance Use cases technologies benefits ...alexjohnson7307
Predictive maintenance is a proactive approach that anticipates equipment failures before they happen. At the forefront of this innovative strategy is Artificial Intelligence (AI), which brings unprecedented precision and efficiency. AI in predictive maintenance is transforming industries by reducing downtime, minimizing costs, and enhancing productivity.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
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- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
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Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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The narrative then shifts to a captivating exploration of prominent desktop OSs, Windows, macOS, and Linux. Windows, with its globally ubiquitous presence and user-friendly interface, emerges as a cornerstone in personal computing history. macOS, lauded for its sleek design and seamless integration with Apple's ecosystem, stands as a beacon of stability and creativity. Linux, an open-source marvel, offers unparalleled flexibility and security, revolutionizing the computing landscape. 🖥️
Moving to the realm of mobile devices, Das unravels the dominance of Android and iOS. Android's open-source ethos fosters a vibrant ecosystem of customization and innovation, while iOS boasts a seamless user experience and robust security infrastructure. Meanwhile, discontinued platforms like Symbian and Palm OS evoke nostalgia for their pioneering roles in the smartphone revolution.
The journey concludes with a reflection on the ever-evolving landscape of OS, underscored by the emergence of real-time operating systems (RTOS) and the persistent quest for innovation and efficiency. As technology continues to shape our world, understanding the foundations and evolution of operating systems remains paramount. Join Pravash Chandra Das on this illuminating journey through the heart of computing. 🌟
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Ivanti’s Patch Tuesday breakdown goes beyond patching your applications and brings you the intelligence and guidance needed to prioritize where to focus your attention first. Catch early analysis on our Ivanti blog, then join industry expert Chris Goettl for the Patch Tuesday Webinar Event. There we’ll do a deep dive into each of the bulletins and give guidance on the risks associated with the newly-identified vulnerabilities.
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2. “Traditional” personalization
on the World Wide Web
64%
48%
48%
23%
23%
23%
20%
16%
11%
9%
7%
5%News clipping services
Personalized content through non-PC devices
Custom pricing
Targeted marketing/advertising
Express transactions
Saved links
Product recommendations
Wish lists
Personal productivity tools
Account access
Customized content
Tailored email alerts
Percent of 44 companies interviewed (multiple responses accepted) Source: Forrester Research
3. More recent deployed personalization
• Personalized search
• Web courses that tailor their teaching strategy to each individual
student
• Information and recommendations by portable devices that consider
users’ location and habits
• Personalized news (to mobile devices)
• Product descriptions whose complexity is geared towards the
presumed level of user expertise
• Tailored presentations that take into account the user’s preferences
regarding product presentation and media types (text, graphics, video)
4. Current personalization methods
(in 30 seconds)
Data sources
• Explicit user input
• User interaction logs
Methods
• Assignment to user groups
• Rule-based inferences
• Machine learning
Storage of data about users
• Persistent user profile
• Updated over time
5. Web personalization delivers benefits for
both users and web vendors
Jupiter Communications, 1998: Personalization at 25 consumer e-commerce
sites increased the number of new customers by 47% in the first year, and
revenues by 52%.
Nielsen NetRatings, 1999:
• Registered visitors to portal sites spend over 3 times longer at their home
portal than other users, and view 3 to 4 times more pages at their portal
• E-commerce sites offering personalized services convert significantly more
visitors into buyers than those that don’t.
Choicestream 2004, 2005:
• 80% interested in personalized content
• 60% willing to spend a least 2 minutes answering questions about themselves
Downside: Personalized sites collect significantly more personal data
than regular websites, and do this often in a very inconspicuous manner.
6. Many computer users are concerned
about their privacy online
Number of users who reported:
• being extremely or very concerned about divulging personal information online:
67% (Forrester 1999), 74% (AARP 2000)
• being (extremely) concerned about being tracked online:
77% (AARP 2000)
• leaving web sites that required registration information:
41% (Boston Consulting 1997)
• having entered fake registration information:
40% (GVU 1998), 27% (Boston Consulting 1997), 32% (Forrester 1999)
• having refrained from shopping online due to privacy concerns, or bought less:
32% (Forrester 1999), 32% 35% 54% : IBM 1999, 24% (AARP 2000)
• wanting internet sites ask for permission to use personal data: 81% (Pew 2000)
• being willing to give out personal data for getting something valuable in return:
31% (GUV 1998), 30% (Forrester 99), 51% (Personalization Consortium)
7. Privacy surveys do not seem to predict
people’s privacy-related actions very well
Harper and Singleton, 2001
Personalization Consortium
• In several privacy studies in E-commerce contexts, discrepancies
have already been observed between users stating high privacy
concerns but subsequently disclosing personal data carelessly.
• Several authors therefore challenge the genuineness of such
reported privacy attitudes and emphasize the need for experiments
that allow for an observation of actual online disclosure behavior.
8. Either Personalization or Privacy?
• Personal data of computer users are indispensable for
personalized interaction
• Computer users are reluctant to give out personal data
☛ Tradeoff between
privacy and
personalization?
9. The tension between privacy and
personalization is more complex than that…
• Indirect relationship between
privacy and personalization
• Situation-dependent
• Many mitigating factors
People use “privacy calculus” to decide
whether or not to disclose personal data,
e.g. for personalization purposes
10. Privacy-Enhanced Personalization
How can personalized systems
maximize their personalization
benefits, while at the same time
being compliant with the privacy
constraints that are in effect?
Can we have good
personalization and good
privacy at the same time?
11. Privacy constraints,
and how to deal with them
Privacy constraints
A. People’s privacy preferences in a given situation
(and factors that influence them)
B. Privacy norms (laws, self-regulation, principles)
Reconciliation of privacy and personalization
1. Use of privacy-enhancing technology
2. Privacy-minded user interaction design
12. Privacy constraints,
and how to deal with them
Privacy constraints
A. People’s privacy preferences in a given situation
(and factors that influence them)
B. Privacy norms (laws, self-regulation, principles)
Reconciliation of privacy and personalization
1. Use of privacy-enhancing technology
2. Privacy-minded user interaction design
13. A. Factors that influence people’s
willingness to disclose information
Information type
– Basic demographic and lifestyle information, personal
tastes, hobbies
– Internet behavior and purchases
– Extended demographic information
– Financial and contact information
– Credit card and social security numbers
Data values
– Willingness to disclose certain data decreases with
deviance from group average
– Confirmed for age, weight, salary, spousal salary, credit
rating and amount of savings
14. Valuation of personalization
“the consumers’ value for personalization is almost
two times […] more influential than the consumers’
concern for privacy in determining usage of
personalization services”
Chellappa and Sin 2005
What types of personalization do users value?
• time savings, monetary savings, pleasure
• customized content, remembering preferences
15. Trust
Trust positively affects both people’s stated willingness
to provide personal information to websites, and their
actual information disclosure to experimental websites.
Antecedents of trust
• Past experience (of oneself and of others)
Established, long-term relationships are specifically important
• Design and operation of a website
• Reputation of the website operator
• Presence of a privacy statement / seal
• Awareness of and control over the use of personal data
16. Privacy constraints,
and how to deal with them
Privacy constraints
A. People’s privacy preferences in a given situation
(and factors that influence them)
B. Privacy norms (laws, self-regulation, principles)
Reconciliation of privacy and personalization
1. Use of privacy-enhancing technology
2. Privacy-minded user interaction design
17. B. Privacy norms
• Privacy laws
More than 40 countries and 100 states and provinces worldwide
• Industry self-regulations
Companies, industry sectors (NAI)
• Privacy principles
– supra-national (OECD, APEC)
– national (Australia, Canada, New Zealand…)
– member organizations (ACM)
Several privacy laws disallow a number of frequently
used personalization methods unless the user’s consents
18. Privacy constraints,
and how to deal with them
Privacy constraints
A. People’s privacy preferences in a given situation
(and factors that influence them)
B. Privacy norms (laws, self-regulation, principles)
Reconciliation of privacy and personalization
1. Use of privacy-enhancing technology
2. Privacy-minded user interaction design
20. Privacy-Enhancing Technology
for Personalized Systems
Caveats
• PETs are not “complete solutions” to the privacy
problems posed by personalized systems
• Their presence is also unlikely to “charm away” users’
privacy concerns.
PETs should be used judiciously in a user-oriented
system design that takes both privacy attitudes and
privacy norms into account
21. Pseudonymous users and user models
• In most cases privacy laws do not apply any more when users cannot
be identified with reasonable means.
• Pseudonymous users may be more inclined to disclose personal data
(but this needs still to be shown!), leading to better personalization.
• Anonymity is currently difficult and/or tedious to preserve when pay-
ments, physical goods and non-electronic services are exchanged.
• Harbors the risk of misuse.
• Hinders vendors from cross-channel personalization.
• Anonymity of database entries, web trails, query terms, ratings and texts
can be surprisingly well defeated by an attacker who has identified data
available that can be partly matched with the “anonymous” data.
Users of personalized systems can receive full personalization
and remain anonymous (users are unidentifiable, unlinkable and
unobservable for third parties, linkable for personalized system)
+
–
22. • A website with personalization at the client side only is generally not
subject to privacy laws.
• Users may be more inclined to disclose their personal data if
personalization is performed locally upon locally stored data only.
• Popular user modeling and personalization methods that rely on an
analysis of data from the whole user population cannot be applied any
more or will have to be radically redesigned.
• Server-side personalization algorithms often incorporate confidential
rules / methods, and must be protected from unauthorized access.
Trusted computing platforms
Client-side Personalization
Users’ data are all located at the client rather than the server side.
All personalization processes that rely on this data are exclusively
carried out at the client side.
+
–
23. Privacy-enhancing techniques for
collaborative filtering
• Traditional collaborative filtering systems collect large amounts of user data in a
central repository (product ratings, purchased products, visited web pages),
• Central repositories may not be trustworthy, can be targets for attacks.
Privacy-protecting techniques
Distribution: No central repository, but interacting distributed clusters that contain information
about a few users only.
Aggregation of encrypted data
– Users privately maintain their own individual ratings
– Community of users compute an aggregate of their private data with homomorphic encryption and
peer-to-peer communication
– The aggregate allows personalized recommendations to be generated at the client side
Perturbation: Ratings become systematically altered before submission to the central server, to hide
users’ true values from the server.
Obfuscation
– A certain percentage of users’ ratings become replaced by different values before the ratings are
submitted to a central server for collaborative filtering.
– Users can then “plausibly deny” the accuracy of any of their data.
24. Privacy constraints,
and how to deal with them
Privacy constraints
A. People’s privacy preferences in a given situation
(and factors that influence them)
B. Privacy norms (laws, self-regulation, principles)
Reconciliation of privacy and personalization
1. Use of privacy-enhancing technology
2. Privacy-minded user interaction design
25. 2. Privacy-minded user interaction design
Two experiments on the privacy-minded
communication of websites’ privacy practices
26. Current “industry standard”:
Privacy statements
Privacy statements…
• are regarded as very important by 76% (DTI 2001)
• make 55% more comfortable providing personal
information (Roy Morgan 2001, Gartner 2001)
• are claimed to be viewed by 73% [“always” by 26%]
(Harris Interactive 2001)
• are effectively viewed by only 0.5% (Kohavi 2001), 1%
(Reagan 2001)
• are several readability levels too difficult (Lutz 04,
JensenPotts 04)
27. Our counterproposal: A design pattern for
personalized websites that collect user data
1. Traditional link to global privacy statement
• Still necessary for legal reasons
2. Additionally, contextualized local communication of privacy
practices and personalization benefits
• Break long privacy policies into small, understandable pieces
• Relate them specifically to the current context
• Explain privacy practices as well as personalization benefits
Design patterns constitute descriptions of best practices based on research
and application experience. They give designers guidelines for the efficient
and effective design of user interfaces.
Every personalized site that collects user data should include the following
elements on every page:
28. An example webpage based on
the proposed design pattern
Traditional link to a
privacy statement
Explanation of
privacy practices
Explanation of
personalization benefits
29. The tested “experimental new version
of an online bookstore”
Contextualized short description of relevant
privacy practices
(taken from original privacy statement)
Contextualized short description of relevant
personalization benefits
(derived from original privacy statement)
Links to original privacy state-ment
(split into privacy, security and
personalization notice)
“Selection counter”
30. Traditional version,
with link to privacy statement only
Links to original privacy state-
ment (split into privacy, security
and personalization notice)
“Selection counter”
31. Why not simply ask users
what they like better?
• Inquiry-based empirical studies
Reveal aspects of users’ rationale that cannot be inferred
from mere observation
• Observational empirical studies
Reveal actual user behavior which may differ from users’
stated behavior
This divergence seems to be quite substantial in the case of
stated privacy preferences vs. actual privacy-related behavior
32. Experimental Procedures
(partly based on deception)
1. Instructions to subjects
“Usability test with new version of a well-known online book retailer”
Answering questions to allegedly obtain better book recommendations
No obligation to answer any question, but helpful for better recommendation.
Data that subjects entered would purportedly be available to company
Possibility to buy one of the recommended books with a 70% discount.
Reminder that if they buy a book, ID card and bank/credit card would be checked
(subjects were instructed beforehand to bring these documents if they wish to buy)
2. Answering interest questions in order to “filter the selection
set” (anonymous)
• 32 questions with 86/64 answer options become presented (some free-text)
• Most questions were about users’ interests (a very few were fairly sensitive)
• All “make sense” in the context of filtering books that are interesting for readers
• Answering questions decreased the “selection counter” in a systematic manner
• After nine pages of data entry, users are encouraged to review their entries, and
then to view those books that purportedly match their interests
33. Experimental Procedures (cont’d)
3. “Recommendation” of 50 books (anonymous)
• 50 predetermined and invariant books are displayed (popular fiction, politics, travel, sex
and health advisories)
• Selected based on their low price and their presumable attractiveness for students
• Prices of all books are visibly marked down by 70%, resulting in out-of-pocket expenses
between €2 and €12 for a book purchase.
• Extensive information on every book available
4. Purchase of one book (identified)
• Subjects may purchase one book if they wish
• Those who do are asked for their names, shipping and payment data (bank account or
credit card charge).
5. Completing questionnaires
6. Verification of name, address and bank data (if book purchased)
34. Subjects and experimental design
• 58 economics/business/MIS students of Humboldt
University, Berlin, Germany
(data of 6 students were eventually discarded)
• Randomly assigned to one of two system versions:
a) 26 used traditional version (that ony had links to global
descriptions of privacy, personalization and security)
b) 26 used contextualized version (with additional local information
on privacy practices and personalization benefits for each entry
field)
Hypothesis:
– User will be more willing to share personal data
– User will regard enhanced site more favorably in terms of privacy
35. Results
Control
With contextual
explanations
% increase p
Questions
answered
84% 91% +8% 0.001
Answers given 56% 67% +20% 0.001
Book buyers 58% 77% +33% 0.07
“Privacy has
priority”
3.34 3.95 +18% 0.01
“Data is used
responsibly”
3.62 3.91 +8% 0.12
“Data allowed
store to select
better books”
2.85 3.40 +19% 0.035
ObservationsPerception
36. Privacy, Trust and Personalization
Trust
Willingness
to disclose
personal
data
Quality of
personalization
Perceived
benefits
Perceived
privacy
+
+
+
+
Control of
own data
Understanding
+
+
Purchases
+
+
+
+
+
+
37. Other factors to be studied
With
contextual
explanations
w/o contextual
explanations
Increase p
Questions
answered
91% 84% +8% 0.001
Answers given 67% 56% +20% 0.001
Book buyers 77% 58% +33% 0.07
“Privacy has
priority”
3.95 3.34 +18% 0.01
“Data allowed
store to select
better books”
3.40 2.85 +19% 0.035
“Data is used
responsibly”
3.91 3.62 +8% 0.12
ObservationsPerception
• Site reputation
• Is privacy or benefit dis-
closure more important?
• Stringency of privacy
practices
• Permanent visibility of
contextual explanations
• References to full privacy
policy
38. Privacy Finder (Cranor et al.)
P3P
• P3P is a W3C standard that allows a website to create a
machine-readable version of its privacy policy
• P3P policies an be automatically downloaded and evaluated
against an individual's privacy preferences
Privacy Finder is a search engine that
• Displays the search results
• Evaluates the P3P policies of the retrieved sites
• Compares them with the user’s individual preferences
• Displays this information graphically
39. Will online shoppers pay a premium
for privacy? (Tsai et al., WEIS 2007)
• Participants were adults from the general Pittsburgh population
(12.5% privacy-unconcerned were screened out)
• They received a flat fee of $45, and had to buy a battery pack and a sex
toy online (each about $15 with shipping). They could keep the rest.
• Three conditions, each with 16 subjects.
• Subjects had to enter a pre-determined search expression, and were
shown a list of real search results that were artificially ranked and
supplemented with additional information:
– in condition no-info: total price only (with shipping)
– in condition irrelevant-info: total price, accessibility rating
– in condition privacy-info: total price, privacy rating
42. Results
1. Between conditions no-info and accessibility-info:
No significant difference in avg. purchase price
2. Between conditions no-info and privacy info:
Users with privacy-info paid an average premium of $0.59
for batteries, and $0.62 for sex toys (p0.0001)
3. In the absence of prominent privacy information, people
will purchase where price is lowest:
Batteries Sex toys
no-info 83% 80%
irrelevant-info 75% 67%
privacy-info 22% 33%
Percentage of users who
bought at the cheapest site
44. There is no magic bullet for
reconciling personalization with privacy
Effort is comparable to
… making systems secure
… making systems fast
… making systems reliable
45. Privacy-Enhanced Personalization:
Need for a process approach
1. Gain the user’s trust
– Respect the user’s privacy attitude (and let the user know)
• Respect privacy laws / industry privacy agreements
– Provide benefits (including optimal personalization within the given privacy
constraints)
– Increase the user’s understanding (don’t do magic)
– Use trust-enhancing methods
– Give users control
– Use privacy-enhancing technology (and let the user know)
2. Then be patient, and most users will incrementally come forward with
personal data / permissions if the usage purpose for the data and the
ensuing benefits are clear and valuable enough to them.
46.
47. Existing approaches for
catering to privacy constraints
• Largest permissible dominator (e.g., Disney)
– Infeasible if a large number of jurisdictions are involved, since the largest
permissible denominator would be very small
– Individual preferences not taken into account
• Different country/region versions (e.g., IBM)
– Infeasible as soon as the number of countries/regions, and hence the
number of different versions of the personalized system, increases
– Individual preferences not taken into account
• Anonymous personalization (users are not identified)
– Nearly full personalization possible
– Harbors the risk of misuse
– Slightly difficult to implement if physical shipments are involved
– Practical extent of protection unclear
– Individual user preferences not taken into account
48. Our approach
Seeking a mechanism to dynamically select user
modeling components that comply with the currently
prevailing privacy constraints
49. User modeling component pool
Different methods differ in their data requirements,
quality of predictions, and also their privacy implications
data used
demographic
data
user-supplied
data
visited
pages
X
X
X
X X
X X
X X
X X
X X
user
modeling
component
methods used
UMC1
clustering
UMC2
rule-based reasoning
UMC3
fuzzy reasoning with uncertainty
UMC4
rule-based reasoning
UMC5
fuzzy reasoning with uncertainty
UMC6
incremental machine learning
UMC7
one-time machine learning across
sessions
UMC8
one-time machine learning +
fuzzy reasoning with uncertainty
50. Product line architecture
• “The common architecture for a set of related products or
systems developed by an organization.” [Bosch, 2000]
• a PLA includes
– Stable core: basic functionalities
– Options: optional features/qualities
– Variants: alternative features/qualities
• Dynamic runtime selection (van der Hoek 2002):
A particular architecture instance is selected from the
product-line architecture
56. Roadmap for Privacy-Enhanced
Personalization Research
• Study the impacts of privacy laws, industry
regulations and individual privacy preferences on
the admissibility of personalization methods
• Provide optimal personalization while respecting
privacy constraints
• Apply state-of-the-art industry practice for
managing the combinatorial complexity of privacy
constraints
57. Readings…
• A. Kobsa: Privacy-Enhanced Web Personalization.
In P. Brusilovsky, A. Kobsa, W. Nejdl, eds.: The
Adaptive Web: Methods and Strategies of Web
Personalization. Springer Verlag.
• A. Kobsa: Privacy-Enhanced Personalization.
Communications of the ACM, Aug. 2007