APUS: Towards Adaptive Privacy-Aware User Sharing in Social Networks via Multiagent Reinforcement Learning

Online social networks (OSNs) have become the most popular platforms for information creation and sharing, actively encouraging users to showcase personal data such as profiles, opinions, and activities. The paradox of sharing and privacy issues is a big trouble for users. Most privacy protection services provided for users, such as access control, need to be manually configured to follow the privacy-aware user intents. It causes a significant cognitive burden for users, due to the dynamic changes in user relationships and the existence of many malicious users in the network. An adaptive privacy-aware sharing strategy management framework (APUS) based on multi-agent reinforcement learning (MARL) is proposed, which provides a service for users to adjust their sharing strategies dynamically in the context of potential malicious users. In APUS, each agent observes some users and has similar workloads, and MARL is adopted for learning cooperation. Besides, the optimal sharing strategies are dynamically derived to manage users' privacy settings for shared content while preventing the impact of malicious users. Experiments conducted on real-world datasets show that this work can effectively promote a win-win situation between the OSN platform and users.