Rim Ben Salem
ABSTRACT
The proliferation of social media set the foundation for the culture of over-disclosure where many people document every single event, incident, trip, etc. for everyone to see. Raising the individual's awareness of the privacy issues that they are subjecting themselves to can be challenging. This becomes more complex when the post being shared includes data "owned" by others. The existing approaches aiming to assist users in multi-party disclosure situations need to be revised to go beyond preferences to the "good" of the collective.
This paper proposes an agent called Aegis to calculate the potential risk incurred by multi-party members in order to push privacy-preserving nudges to the sharer. Aegis is inspired by the consequentialist approach in normative ethical problem-solving techniques. The main contribution is the introduction of a social media-specific risk equation based on data valuation and the propagation of the post from intended to unintended audience. The proof-of-concept reports on how Aegis performs based on real-world data from the SNAP dataset and synthetically generated networks.
- Kevser Zeynep Meral. 2021. Social Media Short Video-Sharing TikTok Application and Ethics. Multidisciplinary Approaches to Ethics in the Digital Era, 147--165. https://doi.org/10.4018/978-1-7998-4117-3.ch010Google Scholar
- Hanbyul Choi, Jonghwa Park, and Yoonhyuk Jung. 2018. The role of privacy fatigue in online privacy behavior. Computers in Human Behavior 81, 42--51. https://doi.org/10.1016/j.chb.2017.12.001Google ScholarCross Ref
- Jose M. Such and Natalia Criado. 2018. Multiparty privacy in social media. Communications of the ACM 61, 74--81. https://doi.org/10.1145/3208039Google ScholarDigital Library
- Alessandro Acquisti, Laura Brandimarte, and Jeff Hancock. 2022. How privacy's past may shape its future. Science 375, 270--272. https://doi.org/10.1126/science.abj0826Google ScholarCross Ref
- Francesca Mosca, and Jose Such. 2021. ELVIRA: An Explainable Agent for Value and Utility-Driven Multiuser Privacy. Proceedings of the 2021 International Conference on Autonomous Agents and Multi-Agent Systems, https://doi.org/10.48448/TQ4J-ZM21Google Scholar
- Daphne Chang, Erin L. Krupka, Eytan Adar, and Alessandro Acquisti. 2016. Engineering Information Disclosure. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. https://doi.org/10.1145/2858036.2858346Google ScholarDigital Library
- Jose Alemany, Elena Del Val, Juan M. Alberola, and Ana Garcia-Fornes. 2019. Metrics for Privacy Assessment When Sharing Information in Online Social Networks. IEEE Access 7, 143631--143645. https://doi.org/10.1109/access.2019.2944723Google ScholarCross Ref
- Yun Zhang, Si Shi, Shijun Guo, Xiaogang Chen, and Zhirong Piao. 2021. Audience management, online turbulence and lurking in social networking services: A transactional process of stress perspective. International Journal of Information Management 56, 102233. https://doi.org/10.1016/j.ijinfomgt.2020.102233Google ScholarDigital Library
- Francesca Rossi. 2016. Ethical Preference-Based Decision Support Systems. Proceedings of the 2016 International Conference on Concurrency Theory. https://doi.org/10.4230/LIPICS.CONCUR.2016.2Google Scholar
- Julian McAuley and Jure Leskovec. 2012. Discovering Social Circles in Ego Networks. Proceedings of the 2012 International Conference on Neural Information Processing Systems (NIPS). https://doi.org/10.48550/ARXIV.1210.8182Google Scholar
- Sunil Kumar and Vikas Somani. 2018. Social Media Security Risks, Cyber Threats And Risks Prevention And Mitigation Techniques. International Journal of Advance Research in Computer Science and Management 4, 125--129.Google Scholar
- Philip Nyblom, Gaute Wangen, and Vasileios Gkioulos. 2020. Risk Perceptions on Social Media Use in Norway. Future Internet 12, 211. https://doi.org/10.3390/fi12120211Google ScholarCross Ref
- Alessandro Acquisti, Idris Adjerid, Rebecca Balebako, Laura Brandimarte, Lorrie Faith Cranor, Saranga Komanduri, Pedro Giovanni Leon, Norman Sadeh, Florian Schaub, Manya Sleeper, Yang Wang, and Shomir Wilson. 2018. Nudges for Privacy and Security. ACM Computing Surveys 50, 1--41. https://doi.org/10.1145/3054926Google ScholarDigital Library
- Tobias Mirsch, Christiane Lehrer, and Reinhard Jung. 2018. Making Digital Nudging Applicable: The Digital Nudge Design Method. Proceedings of the 2018 International Conference on Information Systems (ICIS).Google Scholar
- Omar Mahmoud. 2016. Book Review: Inside the Nudge Unit: How Small Changes can Make a Big Difference. International Journal of Market Research 58, 155--157. https://doi.org/10.2501/ijmr-2016-010Google ScholarCross Ref
- Karen Renaud, Verena Zimmerman, Joseph Maguire, and Steve Draper. 2017. Lessons Learned from Evaluating Eight Password Nudges in the Wild. 2017 LASER Workshop: Learning from Authoritative Security Experiment Results.Google Scholar
- Karen Renaud and Verena Zimmerman. 2017. Enriched nudges lead to stronger password replacements but implement mindfully. 2017 Information Security for South Africa (ISSA). https://doi.org/10.1109/issa.2017.8251779Google Scholar
- Nicolás E. Díaz Ferreyra, Tobias Kroll, Esma Aïmeur, Stefan Stieglitz, and Maritta Heisel. 2020. Preventative Nudges: Introducing Risk Cues for Supporting Online Self-Disclosure Decisions. Information 11, 399. https://doi.org/10.3390/info11080399Google Scholar
- Sourya Joyee De and Daniel Le Metayer. 2018. Privacy Risk Analysis to Enable Informed Privacy Settings. 2018 IEEE European Symposium on Security and Privacy Workshops. https://doi.org/10.1109/eurospw.2018.00019Google ScholarCross Ref
- Gaurav Misra and Jose M. Such. 2016. How Socially Aware Are Social Media Privacy Controls? Computer 49, 96--99. https://doi.org/10.1109/mc.2016.83Google ScholarDigital Library
- Andrew Besmer and Heather Richter Lipford. 2010. Moving beyond untagging. Proceedings of the 28th international conference on Human factors in computing systems - CHI '10. https://doi.org/10.1145/1753326.1753560Google ScholarDigital Library
- Onuralp Ulusoy and Pinar Yolum. 2020. Collaborative Privacy Management with Auctioning Mechanisms. Advances in Automated Negotiations, 45--62. https://doi.org/10.1007/978-981-15-5869-6_4Google Scholar
- Florian Schaub. 2018. Context-Adaptive Privacy Mechanisms. Handbook of Mobile Data Privacy, 337--372. https://doi.org/10.1007/978-3-319-98161-1_13Google Scholar
- Alessandro Acquisti, Laura Brandimarte, and George Loewenstein. 2015. Privacy and human behavior in the age of information. Science 347, 509--514. https://doi.org/10.1126/science.aaa1465Google ScholarCross Ref
- Alan F. Westin. 1967. Privacy and Freedom.Google Scholar
- Rim Ben Salem, Esma Aïmeur, and Hicham Hage. 2021. The Privacy versus Disclosure Appetite Dilemma: Mitigation by Recommendation. Workshop on Online Misinformation- and Harm-Aware Recommender Systems, ACM Recommender Systems Conference (RecSys), 2021.Google Scholar
- Pardis Emami Naeini, Sruti Bhagavatula, Hana Habib, Martin Degeling, Lujo Bauer, Lorrie Cranor, and Norman Sadeh. 2017. Privacy Expectations and Preferences in an IoT World. Proceedings of the Thirteenth Symposium on Usable Privacy and Security (SOUPS 2017).Google Scholar
- Ned Augenblick and Aaron Bodoh-Creed. 2018. To reveal or not to reveal: Privacy preferences and economic frictions. Games and Economic Behavior 110, 318--329. https://doi.org/10.1016/j.geb.2017.10.014Google ScholarCross Ref
- Spyros Kokolakis. 2017. Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & Security 64, 122--134. https://doi.org/10.1016/j.cose.2015.07.002Google ScholarDigital Library
- Hicham Hage, Esma Aïmeur, and Amel Guedidi. 2020. Understanding the Landscape of Online Deception. Advances in Media, Entertainment, and the Arts, 290--317. https://doi.org/10.4018/978-1-7998-2543-2.ch014Google Scholar
- John N. Hooker. 1996. Three Kinds of Ethics.Google Scholar
- Walter Sinnott-Armstrong. 2003. Consequentialism, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition).Google Scholar
- Stephanie Diepeveen, Tom Ling, Marc Suhrcke, Martin Roland, and Theresa M Marteau. 2013. Public acceptability of government intervention to change health-related behaviours: a systematic review and narrative synthesis. BMC Public Health 13. https://doi.org/10.1186/1471--2458--13--756Google Scholar
- C. Evers, D. R. Marchiori, A. F. Junghans, J. Cremers, and D. T. D. De Ridder. 2018. Citizen approval of nudging interventions promoting healthy eating: the role of intrusiveness and trustworthiness. BMC Public Health 18. https://doi.org/10.1186/s12889-018-6097-yGoogle Scholar
- George R. Milne, George Pettinico, Fatima M. Hajjat, and Ereni Markos. 2016. Information Sensitivity Typology: Mapping the Degree and Type of Risk Consumers Perceive in Personal Data Sharing. Journal of Consumer Affairs 51, 133--161. https://doi.org/10.1111/joca.12111Google ScholarCross Ref
- Nica Latto. 2021. Data Brokers: Everything You Need to Know. Avast. Retrieved June 10, 2022 from https://www.avast.com/c-data-brokers.Google Scholar
- Retrieved June 10, 2022 from https://www.invisibly.comGoogle Scholar
- Heahter Sullivan. 2021. Consumers can get paid for their internet data. Retrieved June 10, 2022 from https://www.fox26houston.com/news/consumers-can-get-paid-for-their-internet-data.Google Scholar
- Christina Farr. 2019. Hospital execs say they are getting flooded with requests for your health data. CNBC. Retrieved June 10, 2022 from https://www.cnbc.com/2019/12/18/hospital-execs-say-theyre-flooded-with-requests-for-your-health-data.html.Google Scholar
- Zachary Ignoffo and Miklos Zoltan. 2022. Dark Web Price Index 2021. Privacy Affairs. Retrieved June 10, 2022 from https://www.privacyaffairs.com/dark-web-price-index-2021/Google Scholar
- Alessandro Acquisti. 2015. Online privacy matters. Perspectives@SMU.Google Scholar
- Jens Grossklags, Alessandro Acquisti, and H John Heinz. 2007. When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To-Protect Personal Information. Economics.Google Scholar
- Alessandro Acquisti, Leslie K. John, and George Loewenstein. 2013. What Is Privacy Worth? The Journal of Legal Studies 42, 249--274. https://doi.org/10.1086/671754Google ScholarCross Ref
- Jeffrey Prince and Scott Wallsten. 2020. How Much is Privacy Worth Around the World and Across Platforms? SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3528386Google Scholar
- Eva-Maria Schomakers, Chantal Lidynia, Dirk Müllmann, and Martina Ziefle. 2019. Internet users' perceptions of information sensitivity -- insights from Germany. International Journal of Information Management 46, 142--150. https://doi.org/10.1016/j.ijinfomgt.2018.11.018Google ScholarDigital Library
- Ereni Markos, George R. Milne, and James W. Peltier. 2017. Information Sensitivity and Willingness to Provide Continua: A Comparative Privacy Study of the United States and Brazil. Journal of Public Policy & Marketing 36, 79--96. https://doi.org/10.1509/jppm.15.159Google ScholarCross Ref
- John M.M. Rumbold and Barbara K. Pierscionek. 2018. What Are Data? A Categorization of the Data Sensitivity Spectrum. Big Data Research 12, 49--59. https://doi.org/10.1016/j.bdr.2017.11.001Google ScholarCross Ref
- Duncan J. Watts. 2002. A simple model of global cascades on random networks. Proceedings of the National Academy of Sciences 99, 5766--5771. https://doi.org/10.1073/pnas.082090499Google ScholarCross Ref
- Jure Leskovec and Andrej Krevl. SNAP Datasets: Stanford Large Network Dataset Collection. Retrieved June 10, 2022 from https://snap.stanford.edu/data/index.html#socnets.Google Scholar
- Aylin Caliskan Islam, Jonathan Walsh, and Rachel Greenstadt. 2014. Privacy Detective. Proceedings of the 13th Workshop on Privacy in the Electronic Society. https://doi.org/10.1145/2665943.2665958Google ScholarDigital Library
- Geetha Raju, Karthika Subbaraj, and Ponnurangam Kumaraguru. 2021. Tweet-scan-post: a system for analysis of sensitive private data disclosure in online social media. Knowledge and Information Systems 63, 2365--2404. https://doi.org/10.1007/s10115-021-01592-2Google ScholarDigital Library
- Yelena Petrykina, Hadas Schwartz-Chassidim, and Eran Toch. 2021. Nudging users towards online safety using gamified environments. Computers & Security 108, 102270. https://doi.org/10.1016/j.cose.2021.102270Google ScholarDigital Library
Index Terms
- Aegis: An Agent for Multi-party Privacy Preservation
Recommendations
Privacy Preservation Techniques for Sequential Data Releasing
IAIT '21: Proceedings of the 12th International Conference on Advances in Information TechnologyPrivacy violation is a serious issue that must be considered when datasets are released for public use. To address this issue, a well-known privacy preservation model, l-Diversity, is proposed. Unfortunately, l-Diversity is generally proposed to ...
Evaluation of a perturbation-based technique for privacy preservation in a multi-party clustering scenario
Data processing techniques and the growth of the internet have resulted in a data explosion. The data that are now available may contain sensitive information that could, if misused, jeopardise the privacy of individuals. In today's web world, the ...
A review of privacy preserving models for multi-party data release framework
WIR '16: Proceedings of the ACM Symposium on Women in Research 2016Nowadays, with the improvement of internet technology and advancement in distributed computing data is increasing rapidly. There is a need of information sharing between organizations. Ideally, we wish to share data from multiple private databases and ...
Comments