International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
YOURPRIVACYPROTECTOR: A RECOMMENDER SYSTEM FOR PRIVACY SETTINGS IN SOCIAL NET...ijsptm
Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behaviour in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social
network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook
users.
Comprehensive Social Media Security Analysis & XKeyscore Espionage TechnologyCSCJournals
Social networks can offer many services to the users for sharing activities events and their ideas. Many attacks can happened to the social networking websites due to trust that have been given by the users. Cyber threats are discussed in this paper. We study the types of cyber threats, classify them and give some suggestions to protect social networking websites of variety of attacks. Moreover, we gave some antithreats strategies with future trends.
YOURPRIVACYPROTECTOR: A RECOMMENDER SYSTEM FOR PRIVACY SETTINGS IN SOCIAL NET...ijsptm
Ensuring privacy of users of social networks is probably an unsolvable conundrum. At the same time, an informed use of the existing privacy options by the social network participants may alleviate - or even prevent - some of the more drastic privacy-averse incidents. Unfortunately, recent surveys show that an average user is either not aware of these options or does not use them, probably due to their perceived complexity. It is therefore reasonable to believe that tools assisting users with two tasks: 1) understanding their social net behaviour in terms of their privacy settings and broad privacy categories, and 2)recommending reasonable privacy options, will be a valuable tool for everyday privacy practice in a social
network context. This paper presents YourPrivacyProtector, a recommender system that shows how simple machine learning techniques may provide useful assistance in these two tasks to Facebook users. We support our claim with empirical results of application of YourPrivacyProtector to two groups of Facebook
users.
Comprehensive Social Media Security Analysis & XKeyscore Espionage TechnologyCSCJournals
Social networks can offer many services to the users for sharing activities events and their ideas. Many attacks can happened to the social networking websites due to trust that have been given by the users. Cyber threats are discussed in this paper. We study the types of cyber threats, classify them and give some suggestions to protect social networking websites of variety of attacks. Moreover, we gave some antithreats strategies with future trends.
Research report on exceesive use of social media lead to mental health issuesHarsh Vardhan
It is a small research on "can excessive use of social media lead to mental health illness". It consists of the tools we used and types of research we used to conduct this research.
The Impacts of Social Networking and Its AnalysisIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Brandtzæg, P.B., & Heim, J. (2009). Why people use social networking sites. Proceedings of the HCI International. (pp. 143–152). In A.A. Ozok and P. Zaphiris (Eds.): Online Communities, LNCS. Springer-Verlag Berlin Heidelberg, San Diego, CA, USA, 19-24 July
Коммуникации на сайт
Онлайн-чат для сайта
Открытые линии
CRM-формы
Обратный звонок с сайта
Автокомпозит
BigData: Облако интересов
Развитие eCommerce D7:
Оформление заказа D7
Раздел покупателя D7
Промоутер скидок и акций
Ритейл продуктов питания
И другие новинки платформы
Ускорение D7 x2
Research report on exceesive use of social media lead to mental health issuesHarsh Vardhan
It is a small research on "can excessive use of social media lead to mental health illness". It consists of the tools we used and types of research we used to conduct this research.
The Impacts of Social Networking and Its AnalysisIJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Modern Engineering Research (IJMER) covers all the fields of engineering and science: Electrical Engineering, Mechanical Engineering, Civil Engineering, Chemical Engineering, Computer Engineering, Agricultural Engineering, Aerospace Engineering, Thermodynamics, Structural Engineering, Control Engineering, Robotics, Mechatronics, Fluid Mechanics, Nanotechnology, Simulators, Web-based Learning, Remote Laboratories, Engineering Design Methods, Education Research, Students' Satisfaction and Motivation, Global Projects, and Assessment…. And many more.
Brandtzæg, P.B., & Heim, J. (2009). Why people use social networking sites. Proceedings of the HCI International. (pp. 143–152). In A.A. Ozok and P. Zaphiris (Eds.): Online Communities, LNCS. Springer-Verlag Berlin Heidelberg, San Diego, CA, USA, 19-24 July
Коммуникации на сайт
Онлайн-чат для сайта
Открытые линии
CRM-формы
Обратный звонок с сайта
Автокомпозит
BigData: Облако интересов
Развитие eCommerce D7:
Оформление заказа D7
Раздел покупателя D7
Промоутер скидок и акций
Ритейл продуктов питания
И другие новинки платформы
Ускорение D7 x2
10 Insightful Quotes On Designing A Better Customer ExperienceYuan Wang
In an ever-changing landscape of one digital disruption after another, companies and organisations are looking for new ways to understand their target markets and engage them better. Increasingly they invest in user experience (UX) and customer experience design (CX) capabilities by working with a specialist UX agency or developing their own UX lab. Some UX practitioners are touting leaner and faster ways of developing customer-centric products and services, via methodologies such as guerilla research, rapid prototyping and Agile UX. Others seek innovation and fulfilment by spending more time in research, being more inclusive, and designing for social goods.
Experience is more than just an interface. It is a relationship, as well as a series of touch points between your brand and your customer. Here are our top 10 highlights and takeaways from the recent UX Australia conference to help you transform your customer experience design.
For full article, continue reading at https://yump.com.au/10-ways-supercharge-customer-experience-design/
http://inarocket.com
Learn BEM fundamentals as fast as possible. What is BEM (Block, element, modifier), BEM syntax, how it works with a real example, etc.
Lightning Talk #9: How UX and Data Storytelling Can Shape Policy by Mika Aldabaux singapore
How can we take UX and Data Storytelling out of the tech context and use them to change the way government behaves?
Showcasing the truth is the highest goal of data storytelling. Because the design of a chart can affect the interpretation of data in a major way, one must wield visual tools with care and deliberation. Using quantitative facts to evoke an emotional response is best achieved with the combination of UX and data storytelling.
How to Build a Dynamic Social Media PlanPost Planner
Stop guessing and wasting your time on networks and strategies that don’t work!
Join Rebekah Radice and Katie Lance to learn how to optimize your social networks, the best kept secrets for hot content, top time management tools, and much more!
Watch the replay here: bit.ly/socialmedia-plan
Content personalisation is becoming more prevalent. A site, it's content and/or it's products, change dynamically according to the specific needs of the user. SEO needs to ensure we do not fall behind of this trend.
Succession “Losers”: What Happens to Executives Passed Over for the CEO Job?
By David F. Larcker, Stephen A. Miles, and Brian Tayan
Stanford Closer Look Series
Overview:
Shareholders pay considerable attention to the choice of executive selected as the new CEO whenever a change in leadership takes place. However, without an inside look at the leading candidates to assume the CEO role, it is difficult for shareholders to tell whether the board has made the correct choice. In this Closer Look, we examine CEO succession events among the largest 100 companies over a ten-year period to determine what happens to the executives who were not selected (i.e., the “succession losers”) and how they perform relative to those who were selected (the “succession winners”).
We ask:
• Are the executives selected for the CEO role really better than those passed over?
• What are the implications for understanding the labor market for executive talent?
• Are differences in performance due to operating conditions or quality of available talent?
• Are boards better at identifying CEO talent than other research generally suggests?
A SMART WIZARD SYSTEM SUITABLE FOR USE WITH INTERNET MOBILE DEVICES TO ADJUST...ijsptm
The privacy of personal information is an important issue affecting the confidence of internet users. The
widespread adoption of online social networks and access to these platforms using mobile devices has
encouraged developers to make the systems and interfaces acceptable to users who seek privacy. The aim
of this study is to test a wizard that allows users to control the sharing of personal information with others.
We also assess the concerns of users in terms of such sharing such as whether to hide personal data in
current online social network accounts. Survey results showed the wizard worked very well and that
females concealed more personal information than did males. In addition, most users who were concerned
about misuse of personal information hid those items. The results can be used to upgrade current privacy
systems or to design new systems that work on mobile internet devices. The system can also be used to save
time when setting personal privacy settings and makes users more aware of items that will be shared with
others.
Implementation of Privacy Policy Specification System for User Uploaded Image...rahulmonikasharma
The regular use of social networking websites and application encompasses the collection and retention of personal and very often sensitive information about users. This information needs to remain private and each social network owns a privacy policy that describes in-depth how user’s information is managed and published. As there is increasing use of images for sharing through social sites, maintaining privacy has become a major problem. In light of these incidents, the need of tools to aid users control access to their shared content is necessary. This problem can be proposed by using an Privacy Policy Specification system to help users compose privacy settings for their shared images. Toward addressing this need, we propose Privacy Policy Specification system to help users to specify privacy settings for their images. Privacy Policy Specification System configure a policy for a group and apply appropriate policies (comment, share, expiry, download) on image for sharing in the group.
Policy resolution of shared data in online social networks IJECEIAES
Online social networks have practically a go-to source for information divulging, social exchanges and finding new friends. The popularity of such sites is so profound that they are widely used by people belonging to different age groups and various regions. Widespread use of such sites has given rise to privacy and security issues. This paper proposes a set of rules to be incorporated to safeguard the privacy policies of related users while sharing information and other forms of media online. The proposed access control network takes into account the content sensitivity and confidence level of the accessor to resolve the conflicting privacy policies of the co-owners.
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.
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.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...ijaia
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
NOVEL MACHINE LEARNING ALGORITHMS FOR CENTRALITY AND CLIQUES DETECTION IN YOU...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of identifying a target demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained from this research could be used for advertising purposes and for building smart recommendation systems. All algorithms were implemented using Python programming language. The experimental results show that we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing clique detection algorithms, the research shown how machine learning algorithms can detect close knit groups within a larger network.
Novel Machine Learning Algorithms for Centrality and Cliques Detection in You...gerogepatton
The goal of this research project is to analyze the dynamics of social networks using machine learning techniques to locate maximal cliques and to find clusters for the purpose of dentifying a target
demographic. Unsupervised machine learning techniques are designed and implemented in this project to analyze a dataset from YouTube to discover communities in the social network and find central nodes. Different clustering algorithms are implemented and applied to the YouTube dataset. The well-known Bron-Kerbosch algorithm is used effectively in this research to find maximal cliques. The results obtained
from this research could be used for advertising purposes and for building smart recommendation systems.
All algorithms were implemented using Python programming language. The experimental results show that
we were able to successfully find central nodes through clique-centrality and degree centrality. By utilizing
clique detection algorithms, the research shown how machine learning algorithms can detect close knit
groups within a larger network.
Identification of inference attacks on private Information from Social Networkseditorjournal
Online social networks, like
Facebook, twitter are increasingly utilized by
many people. These networks permit users to
publish details about them and to connect to
their friends. Some of the details revealed
inside these networks are meant to be
keeping private. Yet it is possible to use
learning algorithms and methods on released
data have to predict private information,
which cause inference attacks. This paper
discovers how to launch inference attacks
using released social networking details to
predict private information’s. It then
separate three possible sanitization
algorithms that could be used in various
situations. Then, it investigates the
effectiveness of these techniques and tries to
use methods of collective inference
techniques to determine sensitive attributes
of the user data set. It shows that it can
decline the effectiveness of both the local and
relational classification algorithms by using
the sanitization methods we described.
Collusion-resistant multiparty data sharing in social networksIJECEIAES
The number of users on online social networks (OSNs) has grown tremendously over the past few years, with sites like Facebook amassing over a billion users. With the popularity of OSNs, the increase in privacy risk from the large volume of sensitive and private data is inevitable. While there are many features for access control for an individual user, most OSNs still need concrete mechanisms to preserve the privacy of data shared between multiple users. The proposed method uses metrics such as identity leakage (IL) and strength of interaction (SoI) to fine-tune the scenarios that use privacy risk and sharing loss to identify and resolve conflicts. In addition to conflict resolution, bot detection is also done to mitigate collusion attacks. The final decision to share the data item is then ascertained based on whether it passes the threshold condition for the above metrics.
Authorization mechanism for multiparty data sharing in social networkeSAT Publishing House
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
The size of the Internet enlarging as per to grow the users of search providers continually demand search
results that are accurate to their wishes. Personalized Search is one of the options available to users in
order to sculpt search results based on their personal data returned to them provided to the search
provider. This brings up fears of privacy issues however, as users are typically anxious to revealing
personal info to an often faceless service provider along the Internet. This work proposes to administer
with the privacy issues surrounding personalized search and discusses ways that privacy can be improved
so that users can get easier with the dismissal of their personal information in order to obtain more precise
search results.
Towards Decision Support and Goal AchievementIdentifying Ac.docxturveycharlyn
Towards Decision Support and Goal Achievement:
Identifying Action-Outcome Relationships From Social
Media
Emre Kıcıman
Microsoft Research
[email protected]
Matthew Richardson
Microsoft Research
[email protected]
ABSTRACT
Every day, people take actions, trying to achieve their per-
sonal, high-order goals. People decide what actions to take
based on their personal experience, knowledge and gut in-
stinct. While this leads to positive outcomes for some peo-
ple, many others do not have the necessary experience, knowl-
edge and instinct to make good decisions. What if, rather
than making decisions based solely on their own personal
experience, people could take advantage of the reported ex-
periences of hundreds of millions of other people?
In this paper, we investigate the feasibility of mining the
relationship between actions and their outcomes from the
aggregated timelines of individuals posting experiential mi-
croblog reports. Our contributions include an architecture
for extracting action-outcome relationships from social me-
dia data, techniques for identifying experiential social media
messages and converting them to event timelines, and an
analysis and evaluation of action-outcome extraction in case
studies.
1. INTRODUCTION
While current structured knowledge bases (e.g., Freebase)
contain a sizeable collection of information about entities,
from celebrities and locations to concepts and common ob-
jects, there is a class of knowledge that has minimal cov-
erage: actions. Simple information about common actions,
such as the effect of eating pasta before running a marathon,
or the consequences of adopting a puppy, are missing. While
some of this information may be found within the free text of
Wikipedia articles, the lack of a structured or semi-structured
representation make it largely unavailable for computational
usage. With computing devices continuing to become more
embedded in our everyday lives, and mediating an increasing
degree of our interactions with both the digital and physical
world, knowledge bases that can enable our computing de-
vices to represent and evaluate actions and their likely out-
comes can help individuals reason about actions and their
Permission to make digital or hard copies of all or part of this work for personal or
classroom use is granted without fee provided that copies are not made or distributed
for profit or commercial advantage and that copies bear this notice and the full citation
on the first page. Copyrights for components of this work owned by others than the
author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or
republish, to post on servers or to redistribute to lists, requires prior specific permission
and/or a fee. Request permissions from [email protected]
KDD’15, August 10-13, 2015, Sydney, NSW, Australia.
Copyright is held by the owner/author(s). Publication rights licensed to ACM.
ACM 978-1-4503-3664-2/15/08 ...$15.00.
DOI: http://dx.doi.org/10.1145 ...
Designing a recommender system based on social networks and location based se...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and preferences in a suggestion provided using just place-based services. Current generation of place-based services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of using social recommender systems was discussed which contains capability of identifying user’s interests and preferences and based on them and user’s current place, it offers some suggestions. Social recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can acquire their favorite information and services.
DESIGNING A RECOMMENDER SYSTEM BASED ON SOCIAL NETWORKS AND LOCATION BASED ...IJMIT JOURNAL
Mobile devices have diminished spatial limitations, in a way that one can personalize content in a suitable
frame considering individual’s location and present it. Yet, it is not possible to consider user’s interests and
preferences in a suggestion provided using just place-based services. Current generation of place-based
services do not provide users with personalized suggestions, instead they just offer suggestions close to
interests based on users distance from the place where they are. In order to solve this problem, the idea of
using social recommender systems was discussed which contains capability of identifying user’s interests
and preferences and based on them and user’s current place, it offers some suggestions. Social
recommender systems are a combination of social data on web like; user’s social networks and spatial
information. Because user’s information include personal information and interests in social network sites,
considering user’s current location and the information existing in social network data base, it is possible
to provide user with a suitable suggestion. Through this method users’ interaction decreases and they can
acquire their favorite information and services.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
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/
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.
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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Assuring Contact Center Experiences for Your Customers With ThousandEyes
Kt3518501858
1. Bisan A. Alsalibi et al Int. Journal of Engineering Research and Application
ISSN : 2248-9622, Vol. 3, Issue 5, Sep-Oct 2013, pp.1850-1858
RESEARCH ARTICLE
www.ijera.com
OPEN ACCESS
CFPRS: Collaborative Filtering Privacy Recommender System
for Online Social Networks
Bisan A. Alsalibi *, Nasriah Zakaria **
Department of Computer Science, Universiti Sains Malaysia, Penang, Malaysia
ABSTRACT
Social-networking sites (SNSs) are known to be among the most prevalent methods of online communication.
Owing to their increasing popularity, online privacy has become a critical issue for these sites. The tools
presently being utilized for privacy settings are too ambiguous for ordinary users to understand and the specified
policies are too complicated. In this paper, a collaborative filtering privacy recommender system is proposed.
The implementation of the system was initiated by examining the users’ attitudes toward privacy; whereby the
most significant factors impacting users’ attitudes towards privacy were determined to be location, religion and
gender. The next step involved the classification of the users into various groups on the basis of the above
factors. The paper presents a method of integrating the identified factors into the collaborative filtering algorithm
to improve the filtering process. The evaluation of results reflects the accuracy of recommendations and proves
that the use of the clustering model assisted the CF recommender in its creation of appropriate recommendations
for each user.
Keywords - Social Networks, Privacy, Collaborative Filtering, Recommender Systems, Privacy Factors.
I.
INTRODUCTION
Social-networking sites such as Facebook
and Twitter have recently become one of the most
remarkable modes of communicating online. The
common purposes of such sites are to connect with
new friends who share common interests, find old
friends, find new job opportunities, receive and
provide recommendations, and much more. The
potential exposure of posting personal information on
such sites can lead to different types of privacy risks,
including identity theft and stalking [1]. The use of
SNSs has continued to grow remarkably quickly,
coupled with considerable shifts in the way users
interact with them. SNSs were, in the past, used
simply as a tool for communication and entertainment,
but now they are incorporated into every aspect of the
daily lives of millions of users, affecting the way they
communicate with each other and do business. The
increasing popularity of online social networks and
people’s extensive adoption of them has raised a
variety of privacy concerns.
The first privacy problem is that socialnetworking sites do not adequately inform their users
about the risks of revealing their private information
online. Although the issue of online privacy has
become a subject of discussion in the media, these
privacy issues are still not considered significant for
many users [2]. Users of SNSs are apparently
unwilling to consider that they might encounter risks
as a result of their activities on SNSs. Even if they
want to protect their privacy, with too much data and
too many friends, it is very difficult for them to
control who can see the activities on their profile
pages. The second problem is that even though users
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of SNSs can control access to their own profile, they
cannot control what others view. It is possible to pass
on information inadvertently, or for personal
information to be posted without one’s permission.
For example, a user can upload an embarrassing photo
of a friend; this photo can also be tagged directly to a
friend’s profile. Moreover, SNS service providers
have unlimited access to users’ data. With this
enormous amount of information, there are many
commercial opportunities for SNSs, such as selling
personal data to third parties. The third problem is that
privacy tools in SNSs are not flexible enough to
protect user’s data properly. For example, the current
Facebook privacy setting GUI is considered to be too
complex for many users [2].
Although some solutions have been
proposed, the privacy problems cannot entirely be
fixed. Therefore, this paper attempts to propose a
privacy recommender system using a collaborative
filtering technique that users can employ to scan their
privacy level and provide them with recommendations
and guidance to minimize privacy violations.
Palestine, as a part of the Arab world and with a
relatively homogenous culture and religion was
chosen as the scope of this project. The main focus of
this paper is to provide a mechanism for analyzing
Palestinians’ group patterns on Facebook based on the
factors that affect their privacy settings, such as
culture, religion, age and gender, and to, propose a
privacy recommender system based on the identified
factors and users’ patterns.
This paper is organized as follows. Section 2
discusses privacy in SNSs in general. Section 3
presents related work. Section 4 concerns the
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collaborative filtering privacy recommender system,
how it works and its implementation phases. Section 5
presents the results and evaluation of CFPRS. Finally,
section 6 concludes the paper.
II.
PRIVACY IN SOCIAL NETWORKS
The main reason for privacy issues in SNSs
is users’ indifference to, or lack of knowledge about,
adjusting their privacy settings. A number of studies
have demonstrated that, although a majority of SNS
users seem to be aware of the availability of privacy
settings and control, they rarely make use of such
controls or change their default settings. Govani and
Pashley examined users’ awareness of privacy issues
and of the existence of privacy settings and controls
provided by Facebook. They found that the vast
majority of users are aware of the possible risks in
making their private information visible to the public
(e.g., identity theft). Nevertheless, users feel
comfortable enough to disclose their private
information. Although almost all of them know how to
limit the visibility of their private information, they do
not take any action to do so [4]. SNSs must inform
users about the consequences of their various activities
while using SNSs and about which part of their
information is accessible and for whom [3].
Furthermore, users need to have powerful and easy-touse tools that enable them to manage the way other
people can access their information in a simple and
flexible way that does not require a lot of time and
effort [3].
III.
RELATED WORK
A number of solutions have been introduced
to tackle the problem of user’s online privacy. One
example is Privacy Suites, proposed by Bonneau [5].
This tool is designed for Facebook users and allows
them to share and adopt another user’s privacy settings.
This approach saves users time and effort, but the
privacy settings for one user may not be suitable for
another user because privacy preferences depend on a
range of factors such as the age, gender, job and
cultural background. Govani and Pashley conducted a
survey which revealed that awareness is not enough to
guarantee privacy protection [4]. Although the users
who participated in their survey cited stalking and
identity theft as their main privacy concerns, they still
reported posting their mobile numbers and real names
on their profiles. This kind of survey may increase user
awareness regarding privacy risks. However, surveys
do not have a perceptible effect on user’ behavior
towards privacy issues. Fang et al. proposed a privacy
recommendation wizard for Facebook privacy settings
[6]. This tool employs data mining and machine
learning methods, including active learning, to provide
feedback regarding existing privacy settings. It
classifies a user’s friends into groups based on their
degree of interconnection, then chooses a sample
friend from each group and asks the user to determine
what items he would like that person to be able to see.
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Based on the user’s answers to these and other
questions about what the user would choose to reveal
to different “friends,” the wizard suggests personalized
privacy settings. This recommendation tool helps users
adjust their privacy settings to match their actual
privacy preferences, especially those who face some
difficulties in dealing with Facebook privacy settings
control. The learning process in this tool takes as input
the answers given by the user about how he would set
privacy for various friends and uses this information to
predict how the user would set privacy settings for his
remaining friends’ list. The learning process is based
on classifying the active user's friends into community
members who are connected to each other. The
drawback is that this classification mechanism cannot
predict the recommendations for a new user who does
not yet have any friend.
In another study, researchers observed that it
is necessary to propose tools that can help SNS users
understand the results of various privacy settings.
Lipford et al. proposed and evaluated an “audience
view” tool, which allows a user to view his profile as it
appears to each of his friends [7]. This interface has
been recently adopted by Facebook. However,
although the audience view helps users understand and
evaluate the correctness of their existing privacy
settings, it does not help them in determining how they
should adjust their settings in order to achieve a safe
configuration. One approach to suggesting privacy
settings to new users is proposed in [8], and the
importance of good initial settings (due to users’
tendency to keep them) is noted. This study presented a
review of how to employ machine learning to
recommend primitive privacy settings that are more
likely to be useful for users. Another helpful aid for
better privacy protection is to provide some
informative metrics by which they can obtain accurate
information. A model which employs data mining and
AI tools can be used to test the level of difficulty
experienced when attempting to access a user’s
information and to inform users about their privacy
risk [9].
IV.
COLLABORATIVE FILTERING PRIVACY
RECOMMENDER SYSTEM (CFPRS)
One of the most widely used techniques in
recommendation systems is collaborative filtering.
Collaborative filtering (CF) is the process of providing
predictions and recommendations (filtering) by
collecting preferences from many users (collaborating).
In order to provide users with appropriate
recommendations that they can use to protect their
privacy, we propose a privacy recommender system
that employs a user-based collaborative filtering
algorithm. User based CF uses a neighborhood-based
algorithm, so called because the system deals with the
user as if he belongs to a group of users sharing the
same interests or the same items. These users are called
neighbors. The system uses information about these
neighbors to make its predictions about the user. The
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system begins by finding other users who share the
same interests or items. Then it gathers all those users’
ratings and begins computing predictions [12]. Our
system is based on the assumption that similar users
are more likely to have similar adjustments to their
privacy settings. The determination of similarity is
obviously a critical step in collaborative filtering. A
Demographic-based CF algorithm is used to find users
who share similar profile data, such as location,
gender, religion, age, and education level. This is the
filtering portion of collaborative filtering. In this paper,
we enhanced the filtering process of the CF algorithm
by identifying the most significant privacy factors
those which have the strongest effect on users’
behaviors with regards to privacy. Those identified
factors will be used as a basis for determining the level
of similarity between users. The implementation of the
system has three parts: (1) identifying the main privacy
factors that affect the user’s behavior regarding privacy
settings; (2) classifying users into clusters on the basis
of the identified factors; and (3) integrating the
identified factors and clusters into the filtering process
of
collaborative
filtering
to
enhance
the
recommendations.
4.1 Privacy Factors
The behavior of users towards privacy
settings for SNSs is affected by a number of factors.
These factors can be classified into two categories:
individual-level factors (e.g., age, gender, and
educational level) and collective level factors (e.g.,
culture, religion). A number of studies have tested to
what extent individual and collective factors affect the
behavior of SNS users towards privacy settings. To
identify the most influential factors among Palestinian
users, a survey was conducted to collect data about
how Palestinian users select their privacy settings in
Facebook. The survey is divided into two sections. The
first section solicits demographic information and
personal data about the user. The second section
concerns general attitudes towards risk and asks the
user to select a preferred privacy setting for each piece
of information in section one. Section one consists of
11 items: name, user name, age, location, gender,
education, religion, email, mobile, languages and user’
profile picture. Section two asks about privacy settings
based on the four levels of privacy available in
Facebook: Public, Friends, Friends of Friends, and
Only Me. Public means that the user’s profile can be
viewed by every SNS user. Friends means that only
people that the user has accepted as Friends can see
his/her profile. Friends of Friends means that the user’s
Friends and people that their friends have identified as
Friends can see his/her profile. Only Me means that the
user’s profile can be viewed by no one but the user
him/herself. Each item in section one is then assigned a
particular privacy level in section two. For instance,
once the user chooses his/her gender in section one, he
chooses the corresponding privacy level from section
two according to who he would like to be able to view
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his gender. Survey results are based on online
responses from 477 participants from four different
locations: South Gaza, North Gaza, Ramallah and
Bethlehem. Participants in the survey consisted of 239
males and 238 females of different ages, religions and
education levels. In order to analyse the data; collected
by the questionnaire and identify the key influencing
factors, the Key Influencers detection process was
used; this determined the key influencer factors in the
output data by applying the naive Bayes algorithm to
the questionnaire data. The naive Bayes algorithm is a
classification method which is mainly based on the
Bayes rule of conditional probability [10]. This method
works by considering that all factors are equally
independent and important for each other. It then starts
to analyze them individually, gradually weighting each
one. The results show that the most influential factors
on privacy settings are location, religion and gender.
These three identified factors were then used as inputs
to the Microsoft clustering algorithm as discussed in
the next section.
4.2 Clustering Process
The behavior pattern of users connected via
social networks can help to predict the dynamics of the
network system [11]. Understanding the behavior of
Palestinian users towards privacy settings is a key
requirement for the design of the collaborative filtering
recommender system. In order to better understand
users’ attitudes and identify privacy settings’ patterns
among the users surveyed, the Microsoft clustering
algorithm was used. The input data for the clustering
algorithm came from the questionnaire described in
Section 4.1. The resulting clusters form around the
most influential factors, thereby identifying them.
Eight different clusters emerged as a result of the
clustering process, as shown in Table 1.
Table 1: The resulted clusters
Cluster
Location
Cluster
1
Cluster
2
Cluster
3
Cluster
4
Cluster
5
Cluster
6
Cluster
7
Cluster
8
North Gaza
North Gaza
South Gaza
South Gaza
North Gaza
North Gaza
Ramallah
Ramallah,
Bethlehem
Religion
Muslim
Muslim
Muslim
Muslim
Christian
Christian
Muslim
Christian
Gende
r
Femal
e
Male
Male
Femal
e
Femal
e
Male
Male
All
Based upon analysis of the survey responses,
privacy trends and Palestinian users’ behavior toward
privacy settings can be clearly seen. Results show that
the vast majority of users tend to hide their email
address and mobile number by adjusting them to the
Only Me privacy level. Furthermore, results show that
the proposed influential privacy factors (location,
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gender and religion) have different effects on the way
users adjusting their privacy settings. Location has a
noticeable impact on privacy settings in the sense that;
users who live in South Gaza are more concerned
about privacy than those who live in North Gaza,
Bethlehem, and Ramallah. South Gaza users tend not
to disclose most of their private information, in
contrast to users from other locations who tend to
disclose most of their profile information. Gender has a
significant effect on privacy settings in that female
users are more likely to be concerned about their online
privacy than male users. The effect of gender can be
illustrated by comparing Cluster Three and Cluster
Four, since the two clusters have the same location and
religion but different genders, as shown in Fig. 1 and
Fig. 2. According to the results, we found that 51% of
male users in Cluster 3 (South Gaza Muslim males)
adjust their mobile privacy to Only Me, whereas 83%
of female users from Cluster 4 (South Gaza Muslim
females) adjust it to Only Me.
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Results show that religion is another factor that
influences the way people adjusting their privacy
settings, in the sense that Muslim users are more
concerned about their privacy than Christians, as seen
in Fig.3. Fig. 3 shows the privacy preferences of
Christian users (male and female) who live in
Ramallah and Bethlehem, with regard to sensitive
information such as mobile number, email, date of
birth, and relationship status ; these items have been set
to Friends privacy level by the majority of users. The
majority of users set their remaining profile
information to Public privacy level. It is notable that,
there is no cluster either for Christians who live in
South Gaza or Muslims who live in Ramallah. The
recommender system will not be able to make
recommendations to a new user if he belongs to a nonexistent cluster. To overcome this limitation, the
recommending system should also be able to provide
recommendations for users based on expert opinions.
In our system, we use expert recommendations based
on the privacy framework proposed by [2].
4.3 The Impact of Privacy Factors on Clustering
Results
As noted above, the behavior of users toward
privacy settings is mainly affected by three factors:
location, gender and religion.
Fig. 1 Cluster 3 trend results
Fig. 2 Cluster 4 trend results
Fig. 3 Cluster 8 trend results
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First, to isolate the effect of gender on user behavior
toward privacy settings, the location and religion
factors must be constant. Fig. 4 shows a comparison
between Cluster One (female Muslims who live in
North Gaza) and Cluster Two (male Muslims who live
in North Gaza) in terms of privacy settings. The
comparison considered the most sensitive profile
information items, which are mobile number, email
address, date of birth and relationship status. From
Fig. 4, we can see that 54% of users belonging to
Cluster One set email privacy to Only Me, whereas
55% of Cluster Two users set it to Friends. Cluster
One users, 68% set mobile privacy to Only Me,
whereas 52% of Cluster Two users set it to Friends.
Cluster One users, 55% set date of birth privacy to
Friends, whereas 32% of Cluster Two users set it to
public. Among Cluster One users, 65% set
relationship status privacy to Friends, whereas only
41% of Cluster Two users set it to Friends. All of
these results demonstrate that female users are more
concerned about privacy than male users. Female
users tend to hide almost all their sensitive information
while using SNS. Secondly, to illustrate the effect of
religion on privacy settings, Fig. 5 shows a comparison
between Cluster One (female Muslims who live in
North Gaza) and Cluster Seven (female Christians who
live in Ramallah and Bethlehem) in terms of how they
adjust their privacy settings. As shown, about 55% of
Muslims from Cluster One set email address to Only
Me, whereas 56% of Christians from Cluster Seven set
it to Friends. In Cluster One, 68% of users set the
mobile privacy to Only Me privacy level, whereas 50%
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of Cluster Seven set it to Friends. Less sensitive
information such as date of birth and relationship status
has a slight difference, as shown in Fig. 5. We can
conclude that Palestinian Muslim users have more
private and closed profiles than their Christian
counterparts.
Fig. 4 Comparison between Cluster 1 and Cluster 2 in
terms of gender effect
Fig. 5 Comparison between Cluster1 and Cluster 7 in
terms of religion effect
Fig. 6 Comparison between Cluster 5 and Cluster 7 in
terms of location
Finally, to illustrate the effect of location on
privacy, Fig. 6 shows a comparison between the users
of Cluster Five (female Christians who live in North
Gaza) and Cluster Seven (female Christians who live
in Ramallah and Bethlehem) in terms of how they
adjust their privacy settings. As shown, about 47% of
users from Cluster 5 set mobile privacy to Only Me,
whereas 48% of users from Cluster 7 set it to Friends
priv. Less sensitive information such as email, date of
birth and relationship status has a slight difference, as
shown in Fig. 6. It is notable that location affects the
behavior of users who have the same religion and the
same gender, implying that people’s privacy
preferences are strongly affected by the culture and
traditions of their surrounding environment.
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4.4 System Design and Implementation
To clarify how this recommender would
work, suppose a social-networking site has a database
full of the private information and privacy settings of
its users. When a new user subscribes to this SNS, he
first has to create a profile. After he fills in his personal
information in the profile form, the system invokes the
user-CF algorithm and starts looking for users who
have profile information similar to him. The similarity
computation is based on the proposed factors that were
identified in Section 4.1:
Location and culture: The collaborative filtering
algorithm starts by finding users who have the
same location in their profiles as the new user’s
original location not where he currently living, but
where he grew up, and where his family, friends
and traditions exist.
Religion: Within this first group, the collaborative
filtering algorithm next finds users whose profiles
list the same religion as the new user.
Gender: Finally, within those who list the same
religion and location, the algorithm finds users
who have the same gender.
After completing these stages, the filtering
results are generated a list of users who have similar
profile information as the new user. The system
retrieves the privacy settings of this group of users in
order to start the second stage. In the second stage, the
system analyzes these users’ settings to determine the
best privacy settings for the new user, and gives them
back to him as recommendations. For example,
suppose that the system needs to recommend privacy
setting for the email address of the new user. The
system counts the number of filtered users who set
their email address to Public and stores it in variable A;
users who set their email address to Friends of Friends
and stores it in variable B. users who set their email
address to Friends, and stores it in variable C; and
users who set their email address to Only Me, and
stores it in variable D. After that After that, the system
compares A, B, C and D, and chooses the maximum
value – that is, the system recommends the user set his
email address to the same privacy level as the majority
of the filtered users. The system repeats this stage for
all the remaining profile items, and then gives the user
the final privacy recommendations.
A major problem limiting the effectiveness of
user based collaborative filtering is the “cold start
problem,” meaning that the algorithm is unable to
return results when the database is empty or almost
empty. In our case, the privacy recommender system
would fail if the SNS database had few or no existing
users to analyze. In this case, we solved the problem
by using expert opinion according to the privacy
framework proposed by [2], as shown in Table 2.
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Table 2: Recommended privacy based on expert
opinions
Recommende
Informatio Information Sensitivit
d
n/ Item
Type
y
Privacy
setting
Custom
Poisonou
Name
Identity
(Best
s
Friends)
User Name
Identity
Healthy
All Friends
Demograph Harmles
Gender
All Friends
ic
s
Demograph Harmles
Age
All Friends
ic
s
Custom
Demograph Poisonou
Location
(Best
ic
s
Friends)
Demograph Harmles
Education
All Friends
ic
s
Demograph Harmles
Religion
All Friends
ic
s
Custom
(Best,
Demograph Harmles
Relationshi
Normal and
ic
s
p
casual
Friends)
Custom
Poisonou
Email
Identity
(Best
s
Friends)
Custom
Poisonou
Mobile
Identity
(Best
s
Friends)
Demograph Harmles
Language
All Friends
ic
s
Custom
(Best,
Profile
Identity
Harmful
Normal and
Picture
casual
Friends)
V.
CFPRS EVALUATION
Collaborative filtering evaluation measures
how well and accurately the CF recommender system
is accomplishing the task of providing appropriate
recommendations. Evaluating the recommender
system’s algorithms is often difficult because
researchers within this field tend to use different
metrics when evaluating the results of their
experiments. This lack of standardization makes it
difficult to compare the various available algorithms
and to determine the most suitable algorithm for a
particular purpose. This problem was investigated by
Herlocker et al. in his study about collaborative
filtering evaluation metrics [13]. He said that choosing
a suitable dataset for a particular algorithm is one of
the most difficult issues when evaluating collaborative
filtering algorithms because different algorithms may
behave better or worse on different datasets. The
effectiveness of the CF recommender system for
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privacy settings can be measured by the extent to
which users are satisfied with the recommendations
they are given, i.e. whether they feel that the
recommended privacy settings are suitable for their
religion, gender and culture. In addition to user
satisfaction metrics, the performance of the CF privacy
recommender system can be measured by its accuracy,
or how close its recommended ranking is to the actual
ranking given by the user to that item, the accuracy can
be measured using the Mean Absolute Error MAE as
illustrated in Eq. (1). The third metric which can be
used in evaluating a CF recommender system is its
precision percentage, which is the percentage of the
correct predictions recommended by the system as
shown in Eq. (2).
(1)
(2)
5.1 Data Collection
The performance of a CF recommender
system can be measured by splitting a user-rating
dataset into two sets, a training dataset and a testing
dataset. The error is measured on the testing dataset
predictions after the algorithm has been fed with the
training ratings. For this study, the training dataset
consists of 477 users and the testing dataset consists of
80 users. Ten users per cluster were asked to evaluate
the system. In our study, we used convenience
sampling. Convenience sampling is a method in which
the researcher chooses samples because of accessibility
[14].
To collect data, we placed posts on Facebook seeking
help with our study; the posts included a link to our
online questionnaire. We also emailed friends of
different locations, gender and religions asking them
to complete our questionnaire. We used nonprobabilistic sampling, which does increase the level
of sampling error. However, we argue that this is an
inevitable problem that researchers face when doing
research within a short time-frame, as demonstrated
by the fact that numerous researchers have used this
method in published articles in peer-reviewed
journals, as previously exemplified.
5.2 Evaluation Results
The experimental results of evaluating our
CFPRS are shown in Table 3. The table includes the
evaluation results of each Cluster, and the final row
gives the average evaluation results for the whole
system. Recall that the CFPRS has 4 privacy levels
(Public, Friends of Friends, Friends, and Only Me)
and 11 personal information items. Each privacy level
has its own associated rank, from 1 to 4. In this case,
according to Eq. (1), the maximum MAE is 3. The
lower the MAE, the more accurately the
recommendation engine predicts user privacy settings.
The average mean absolute error of the CF
recommender system is 0.217045463 out of 3, which
reflects a high accuracy of the privacy settings
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recommended by the system. The average user rate of
the CF recommender system is 4.5875 out of 5; this
reflects to what extent the users are satisfied with the
recommendations given by the system. The average
precision of the system is around 92.15, which
indicates the ability of the CF recommender system to
generate appropriate privacy setting recommendations
which are close to users’ desired settings. It is
noticeable from Table 3 that Cluster Eight (Christians
who live in Ramallah and Bethlehem) has the lowest
MAE and the highest precision. In contrast, Cluster
Five (female Christians who live in North Gaza) has
the highest MAE and the lowest precision.
Table 3: Evaluation results
User
Precisio
N0.
MAE
Rate
n
Cluster 0.163
4.7
92.7272
1
6
Cluster 0.218
4.6
93.6363
2
1
Cluster 0.318
4.5
90.9090
3
1
Cluster 0.118
4.8
95.4545
4
1
Cluster 0.336
4.4
85.4545
5
3
Cluster 0.209
4.5
93.6363
6
0
Cluster 0.272
4.4
89.0909
7
7
Cluster
0.1
4.8
96.3636
8
0.217
92.1590
Overall
4.5875
0
9
It can be clearly seen from Table 3 that we
obtained MAE values very close to 0 (ranging from
0.1-0.33). This means that the CF recommender system
is very accurate and provides appropriate
recommendations to the users. Although the MAE of
some clusters, such as Three and Five, could be
considered a little high compared to other clusters, this
does not necessarily mean that the recommendations
given to users in those clusters are not good. The MAE
is an inadequate measure in some cases since it focuses
exclusively on the accuracy of the predictions without
consideration for their influence on the user’s decisions
[13]. To overcome this limitation, precision and user
satisfaction metrics are used.
It is evident from Table 3 that Cluster Eight
(Christians who live in Ramallah and Bethlehem) and
Cluster Four (female Muslims who live in South Gaza)
have the lowest mean absolute errors (0.1 and 0.118
respectively) and the highest precision and user
satisfaction (96.36%, 4.8 and 95.45%, 4.8
respectively).
The
high
accuracy
of
the
recommendations given to users of Clusters 8 and 4 is
due to the privacy trend results for these clusters. The
cluster results demonstrate that the vast majority of
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users belonging to Clusters 8 and 4 have similar
privacy setting trends. As we can see in Fig. 7 and Fig.
2 respectively, about 65% of users have similar ways
of adjusting their privacy settings. This similarity
increases the probability that the active user (the user
for whom the system is generating recommendations)
will have a high degree of similarity to his cluster
trend. The reason why Cluster Eight users have similar
privacy setting opinions is due to the location and
religion factors, in the sense that Bethlehem is full of
Christians who have the same pure culture which has
not been affected by the Muslim culture, in contrast to
what happened to Christians in other parts of Palestine.
Fig. 7 Cluster 8 trend results
The same reasoning can be applied to Cluster
4, Muslim users who live in South Gaza, where female
Muslims
from
South
Gaza
have many rules and religious restrictions that they all
have to follow. In contrast to Cluster 8 and Cluster 4,
Cluster 5 (female Christians who live in North Gaza)
has the highest mean absolute error (0.336) and the
lowest precision and user satisfaction (85.45% and 4.4
respectively).
The
low
accuracy
of
the
recommendations suggested by the CF recommender
system for Cluster 5 users is due to the fact that
Islamic cultural norms have a large impact on
Muslims and the Christian minority alike.
The vast majority of North Gaza populations are
Muslims which means that Islam is the dominant
religion there. Despite being of a different religion,
some Christians may be more likely to express greater
observance to Islamic cultural norms due to their
deferential social status in Gaza and some of them
may refuse to accept these Islamic norms. This
difference in the attitude of Christians has led to the
contrast in behavior towards privacy settings.
Fig. 8 Cluster 5 trend results
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It is noticeable from Fig. 8 that users of this Cluster
vary in the way they adjust their privacy settings,
whereby less than 50% of users have the same settings
for most of their information; this increases the
probability that the active user will have dissimilar
settings to what their Cluster suggests.
VI.
CONCLUSION AND FUTURE WORK
Online privacy has emerged as an important
problem in online social-networking sites. While these
sites have recently become one of the most prevalent
modes of online communication, the tools available
for privacy settings are still difficult for ordinary users
to understand and use. This paper proposed a CF
privacy recommender system that would help every
user to adjust his privacy settings according to the
group of people and culture he belongs to. Users who
are not aware of the importance of protecting their
online privacy, as well as those who are aware but
cannot use the complicated privacy tools, can both
benefit from using the privacy recommender system.
The implementation phase of the system was initiated
by understanding users’ reactions towards privacy
settings. During this stage, the factors that have the
most impact on Palestinian users’ attitudes towards
privacy were identified. We identified three major
factors, which are location, religion and gender. The
next step was to classify Palestinian users into
different groups based on the three proposed factors.
Users from each group have similar ways of adjusting
their privacy settings. Finally, we have presented an
approach to integrate these identified factors and
similarities into a collaborative filtering algorithm to
implement a recommender system. The main goal of
the integration step is to enhance the filtering process
of collaborative filtering so that it can classify users
efficiently according to their location, religion and
gender.
To evaluate the CF privacy recommender
system, it was tested by ten users from each Cluster.
The evaluation results reflect the accuracy of the
recommendations and prove that using the clustering
model helps the CF recommender to generate
appropriate recommendations suitable for each user.
Such a CF privacy recommender system has the
potential to become a powerful technology that can be
used by many people across a wide range of SNSs.
In the future, we plan to conduct more studies on the
factors that affect privacy to understand more deeply
the ways in which each group of users adjust their
privacy settings. For example, it would be interesting
to enhance the location factor by extending the study
to more locations in Palestine. Since the scope in this
research was centered on Palestinian users, the study
could be expanded in the future to include (for
example) all Asian users. The generalization could be
done by following the same procedure as in this study.
The first step would be to conduct a survey to collect
data and then identify the main privacy factors for
those users. The second step would be to use the
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identified factors as the basis for the classification
process in order to identify user patterns. And finally,
the classification results would be integrated with the
collaborative filtering algorithm to implement the
privacy recommender system.
In our approach, we chose a user-based CF
wherein we seek a community of similar users who
share the same location, religion and gender as the
active user. The recommendations in our technique are
based on the opinion of the majority of similar users.
We are aware that the opinion of the majority is not
true for everyone, because it is based on user
background and not given by trusted users. In the
future, we plan to integrate an expert-based CF with
the user-based CF in order to improve the accuracy of
the recommendations. A weighting scheme will be
needed to balance the two methods. In this case, the
selection of similar users will not only depend on
similar demographic characteristics as the active user,
but will also include expert users from the same
community as the active user. The recommendations
for that community should become more precise by
virtue of these users being more knowledgeable about
the best privacy settings for users in each community.
VII.
ACKNOWLEDGMENTS
B. Alsalibi would like to acknowledge and
extend her heartfelt gratitude to Eng. Abdulla ElMadhoun who has made the completion of this paper
possible and for assisting in the implementation of the
algorithm.
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