Skip to main content
SpringerLink
Log in

Agents for Preserving Privacy: Learning and Decision Making Collaboratively

  • Conference paper
  • First Online:
Multi-Agent Systems and Agreement Technologies (EUMAS 2020, AT 2020)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12520))

Abstract

Privacy is a right of individuals to keep personal information to themselves. Often online systems enable their users to select what information they would like to share with others and what information to keep private. When an information pertains only to a single individual, it is possible to preserve privacy by providing the right access options to the user. However, when an information pertains to multiple individuals, such as a picture of a group of friends or a collaboratively edited document, deciding how to share this information and with whom is challenging as individuals might have conflicting privacy constraints. Resolving this problem requires an automated mechanism that takes into account the relevant individuals’ concerns to decide on the privacy configuration of information. Accordingly, this paper proposes an auction-based privacy mechanism to manage the privacy of users when information related to multiple individuals are at stake. We propose to have a software agent that acts on behalf of each user to enter privacy auctions, learn the subjective privacy valuations of the individuals over time, and to bid to respect their privacy. We show the workings of our proposed approach over multiagent simulations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Acquisti, A., Brandimarte, L., Loewenstein, G.: Privacy and human behavior in the age of information. Science 347(6221), 509–514 (2015)

    Article  Google Scholar 

  2. Bahri, L., Carminati, B., Ferrari, E.: Decentralized privacy preserving services for online social networks. Online Soc. Netw. Media 6, 18–25 (2018)

    Article  Google Scholar 

  3. Barto, A.G., Mahadevan, S.: Recent advances in hierarchical reinforcement learning. Discr. Event Dyn. Syst. 13(4), 341–379 (2003)

    Article  MathSciNet  Google Scholar 

  4. Clarke, E.: Multipart pricing of public goods. Pub. Choice 11(1), 17–33 (1971)

    Article  Google Scholar 

  5. Diuk, C., Cohen, A., Littman, M.L.: An object-oriented representation for efficient reinforcement learning. In: Proceedings of the 25th International Conference on Machine Learning, pp. 240–247. ICML 2008, ACM, New York, NY, USA (2008)

    Google Scholar 

  6. Ephrati, E., Rosenschein, J.S.: The clarke tax as a consensus mechanism among automated agents. In: Proceedings of the Ninth National Conference on Artificial Intelligence, Vol. 1, pp. 173–178. AAAI 1991, AAAI Press (1991)

    Google Scholar 

  7. Fang, L., LeFevre, K.: Privacy wizards for social networking sites. In: Proceedings of the 19th International Conference on World Wide Web, pp. 351–360. WWW 2010, ACM, New York, NY, USA (2010)

    Google Scholar 

  8. Fogues, R.L., Murukannaiah, P.K., Such, J.M., Singh, M.P.: SoSharP: recommending sharing policies in multiuser privacy scenarios. IEEE Internet Comput. 21(6), 28–36 (2017)

    Article  Google Scholar 

  9. Fong, P.W.: Relationship-based access control: protection model and policy language. In: Proceedings of the First ACM Conference on Data and Application Security and Privacy, pp. 191–202. CODASPY 2011, ACM (2011)

    Google Scholar 

  10. Gürses, S., Troncoso, C., Diaz, C.: Engineering privacy by design. Comput. Priv. Data Prot. 14(3), 25 (2011)

    Google Scholar 

  11. Hu, H., Ahn, G.J., Jorgensen, J.: Multiparty access control for online social networks: model and mechanisms. IEEE Trans. Knowl. Data Eng. 25(7), 1614–1627 (2013)

    Article  Google Scholar 

  12. Kekulluoglu, D., Kökciyan, N., Yolum, P.: Preserving privacy as social responsibility in online social networks. ACM Trans. Internet Technol. 18(4), 42:1–42:22 (2018)

    Article  Google Scholar 

  13. Kökciyan, N., Yaglikci, N., Yolum, P.: An argumentation approach for resolving privacy disputes in online social networks. ACM Trans. Internet Technol. 17(3), 27:1–27:22 (2017)

    Article  Google Scholar 

  14. Lampinen, A., Lehtinen, V., Lehmuskallio, A., Tamminen, S.: We’re in it together: interpersonal management of disclosure in social network services. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3217–3226. CHI 2011, ACM, New York, NY, USA (2011)

    Google Scholar 

  15. Langheinrich, M.: Privacy by design — principles of privacy-aware ubiquitous systems. In: Abowd, G.D., Brumitt, B., Shafer, S. (eds.) UbiComp 2001. LNCS, vol. 2201, pp. 273–291. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45427-6_23

    Chapter  MATH  Google Scholar 

  16. Paci, F., Squicciarini, A., Zannone, N.: Survey on access control for community-centered collaborative systems. ACM Comput. Surv. 51(1), 6:1–6:38 (2018)

    Article  Google Scholar 

  17. Spiekermann, S., Cranor, L.F.: Engineering privacy. IEEE Trans. Softw. Eng. 35(1), 67–82 (2009)

    Article  Google Scholar 

  18. Squicciarini, A., Caragea, C., Balakavi, R.: Toward automated online photo privacy. ACM Trans. Web 11(1), 2:1–2:29 (2017)

    Article  Google Scholar 

  19. Squicciarini, A.C., Lin, D., Sundareswaran, S., Wede, J.: Privacy policy inference of user-uploaded images on content sharing sites. IEEE Trans. Knowl. Data Eng. 27(1), 193–206 (2015)

    Article  Google Scholar 

  20. Squicciarini, A.C., Shehab, M., Paci, F.: Collective privacy management in social networks. In: Proceedings of the 18th International Conference on World Wide Web, pp. 521–530. WWW 2009, ACM, New York, NY, USA (2009)

    Google Scholar 

  21. Such, J.M., Rovatsos, M.: Privacy policy negotiation in social media. ACM Trans. Auton. Adapt. Syst. 11(1), 41–429 (2016)

    Article  Google Scholar 

  22. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  23. Tan, M.: Multi-agent reinforcement learning: Independent vs. cooperative agents. In: Proceedings of the Tenth International Conference on Machine Learning, pp. 330–337. Morgan Kaufmann (1993)

    Google Scholar 

  24. Ulusoy, O., Yolum, P.: Collaborative privacy management with auctioning mechanisms. In: Ito, T., Zhang, M., Aydoğan, R. (eds.) ACAN 2018. SCI, vol. 905, pp. 45–62. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-5869-6_4

    Chapter  Google Scholar 

  25. Vanetti, M., Binaghi, E., Ferrari, E., Carminati, B., Carullo, M.: A system to filter unwanted messages from OSN user walls. IEEE Trans. Knowl. Data Eng. 25(2), 285–297 (2013)

    Article  Google Scholar 

  26. Zhong, H., Squicciarini, A., Miller, D.: Toward automated multiparty privacy conflict detection. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management, pp. 1811–1814. CIKM 2018, ACM, New York, NY, USA (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Utrecht University, Utrecht, The Netherlands

    Onuralp Ulusoy & Pınar Yolum

Authors
  1. Onuralp Ulusoy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pınar Yolum
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Onuralp Ulusoy .

Editor information

Editors and Affiliations

  1. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Nick Bassiliades

  2. Technical University of Crete, Chania, Greece

    Georgios Chalkiadakis

  3. IIIA-CSIC, Bellaterra, Spain

    Dave de Jonge

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ulusoy, O., Yolum, P. (2020). Agents for Preserving Privacy: Learning and Decision Making Collaboratively. In: Bassiliades, N., Chalkiadakis, G., de Jonge, D. (eds) Multi-Agent Systems and Agreement Technologies. EUMAS AT 2020 2020. Lecture Notes in Computer Science(), vol 12520. Springer, Cham. https://doi.org/10.1007/978-3-030-66412-1_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-66412-1_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-66411-4

  • Online ISBN: 978-3-030-66412-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation