research-article

Aegis: An Agent for Multi-party Privacy Preservation

Authors:

Hicham HageAuthors Info & Claims

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

Pages 68 - 77

https://doi.org/10.1145/3514094.3534134

Published: 27 July 2022 Publication History

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.

References

[1]

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.ch010

[2]

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.2017.12.001

[3]

Jose M. Such and Natalia Criado. 2018. Multiparty privacy in social media. Communications of the ACM 61, 74--81. https://doi.org/10.1145/3208039

Digital Library

[4]

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.abj0826

[5]

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-ZM21

[6]

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/10.1145/2858036.2858346

Digital Library

[7]

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.2019.2944723

[8]

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.2020.102233

Digital Library

[9]

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.2016.2

[10]

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.1210.8182

[11]

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.

[12]

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/fi12120211

[13]

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/10.1145/3054926

Digital Library

[14]

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).

[15]

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-2016-010

[16]

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.

[17]

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.2017.8251779

[18]

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/info11080399

[19]

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.2018.00019

[20]

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.2016.83

Digital Library

[21]

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/10.1145/1753326.1753560

Digital Library

[22]

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_4

[23]

Florian Schaub. 2018. Context-Adaptive Privacy Mechanisms. Handbook of Mobile Data Privacy, 337--372. https://doi.org/10.1007/978-3-319-98161-1_13

[24]

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.aaa1465

[25]

Alan F. Westin. 1967. Privacy and Freedom.

[26]

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.

[27]

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).

[28]

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.2017.10.014

[29]

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.2015.07.002

Digital Library

[30]

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.ch014

[31]

John N. Hooker. 1996. Three Kinds of Ethics.

[32]

Walter Sinnott-Armstrong. 2003. Consequentialism, The Stanford Encyclopedia of Philosophy (Fall 2021 Edition).

[33]

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--756

[34]

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-y

[35]

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.12111

[36]

Nica Latto. 2021. Data Brokers: Everything You Need to Know. Avast. Retrieved June 10, 2022 from https://www.avast.com/c-data-brokers.

[37]

Retrieved June 10, 2022 from https://www.invisibly.com

[38]

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.

[39]

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.

[40]

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/

[41]

Alessandro Acquisti. 2015. Online privacy matters. Perspectives@SMU.

[42]

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.

[43]

Alessandro Acquisti, Leslie K. John, and George Loewenstein. 2013. What Is Privacy Worth? The Journal of Legal Studies 42, 249--274. https://doi.org/10.1086/671754

[44]

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.3528386

[45]

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.2018.11.018

Digital Library

[46]

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.15.159

[47]

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.2017.11.001

[48]

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.082090499

[49]

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.

[50]

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/10.1145/2665943.2665958

Digital Library

[51]

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/s10115-021-01592-2

Digital Library

[52]

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.2021.102270

Digital Library

Cited By

Salehzadeh Niksirat KKorka DHarkous HHuguenin KCherubini M(2023)On the Potential of Mediation Chatbots for Mitigating Multiparty Privacy Conflicts - A Wizard-of-Oz StudyProceedings of the ACM on Human-Computer Interaction10.1145/35796187:CSCW1(1-33)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579618
Ben Salem RAïmeur EHage H(2023)User modelling for privacy-aware self-disclosure2023 20th Annual International Conference on Privacy, Security and Trust (PST)10.1109/PST58708.2023.10320200(1-8)Online publication date: 21-Aug-2023
https://doi.org/10.1109/PST58708.2023.10320200

Index Terms

Aegis: An Agent for Multi-party Privacy Preservation
1. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Privacy protections

Recommendations

Privacy Preservation Techniques for Sequential Data Releasing
IAIT '21: Proceedings of the 12th International Conference on Advances in Information Technology

Privacy 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 2016

Nowadays, 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

Information & Contributors

Information

Published In

cover image ACM Conferences

AIES '22: Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society

July 2022

939 pages

ISBN:9781450392471

DOI:10.1145/3514094

General Chairs:
Vincent Conitzer
Duke University & University of Oxford
,
John Tasioulas
University of Oxford
,
Program Chairs:
Matthias Scheutz
Tufts University
,
Ryan Calo
University of Washington
,
Martina Mara
Johannes Kepler University Linz
,
Annette Zimmermann
University of York

Copyright © 2022 ACM.

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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 July 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

Canada?s Natural Sciences and Engineering Research Council (NSERC)

Conference

AIES '22

Sponsor:

SIGAI

AIES '22: AAAI/ACM Conference on AI, Ethics, and Society

May 19 - 21, 2021

Oxford, United Kingdom

Acceptance Rates

Overall Acceptance Rate 61 of 162 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
219
Total Downloads

Downloads (Last 12 months)49
Downloads (Last 6 weeks)4

Reflects downloads up to 07 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Salehzadeh Niksirat KKorka DHarkous HHuguenin KCherubini M(2023)On the Potential of Mediation Chatbots for Mitigating Multiparty Privacy Conflicts - A Wizard-of-Oz StudyProceedings of the ACM on Human-Computer Interaction10.1145/35796187:CSCW1(1-33)Online publication date: 16-Apr-2023
https://dl.acm.org/doi/10.1145/3579618
Ben Salem RAïmeur EHage H(2023)User modelling for privacy-aware self-disclosure2023 20th Annual International Conference on Privacy, Security and Trust (PST)10.1109/PST58708.2023.10320200(1-8)Online publication date: 21-Aug-2023
https://doi.org/10.1109/PST58708.2023.10320200

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents