Assisting Users in Privacy Conflicts with Partially Observable Multi-Agent Reinforcement Learning

Hüseyin Aydln*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Avoiding violations of privacy-invading technologies is difficult enough for an individual, yet the complexity escalates when online collaborations and social media jeopardize the privacy of multiple parties over co-owned contents. While existing approaches offer solutions for possible conflicts among users' privacy preferences, they either assume static rules for the preferences of users or require the users to declare separate decisions for each content. In any case, the long term satisfaction of all users remains uncertain. Reinforcement learning (RL) emerges at this point as a suitable candidate for balancing the users' utilities as their satisfactions about decisions over time. The decentralized and dynamic nature of the problem suggests an RL setting that involves multiple agents interacting not only with the humans whom they model and represent but also with each other. Furthermore, as the knowledge of agents about the factors that lead to other users' preferences will be limited, the setting has to handle partial observability. Although this introduces new challenges for the framework, it also brings a potential generalization of any solution to multi-party conflicts in different real life contexts with minor adaptations. This study delves deeper into the features of the proposed framework and the ways to construct it.

Original languageEnglish
Title of host publicationHHAI 2024
Subtitle of host publicationHybrid Human AI Systems for the Social Good - Proceedings of the 3rd International Conference on Hybrid Human-Artificial Intelligence
EditorsFabian Lorig, Jason Tucker, Adam Dahlgren Lindstrom, Frank Dignum, Pradeep Murukannaiah, Andreas Theodorou, Pinar Yolum
PublisherIOS Press
Pages55-63
Number of pages9
ISBN (Electronic)9781643685229
DOIs
Publication statusPublished - 5 Jun 2024
Event3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024 - Hybrid, Malmo, Sweden
Duration: 10 Jun 202414 Jun 2024

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume386
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

Conference3rd International Conference on Hybrid Human-Artificial Intelligence, HHAI 2024
Country/TerritorySweden
CityHybrid, Malmo
Period10/06/2414/06/24

Keywords

  • human-AI collaboration
  • multi-agent systems
  • multi-party privacy conflicts
  • reinforcement learning
  • user modeling

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