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

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.