Learning Privacy Rules Cooperatively in Online Social Networks
Berkant Kepez, Pınar Yolum · 2016
The use of online social networks is growing rapidly. With this rapid increase, preserving privacy of users is becoming harder and harder. Typically, social networks address the privacy problem by asking users to define their privacy constraints up front. However, many times deciding on whom to show a post is dependent on the post itself and its context. Hence, users are forced to configure each post specifically, which is both cumbersome and prone to error. Accordingly, this paper first proposes an approach that suggests privacy configurations for each post. The suggestions are based on learning from users' previous posts and configurations. However, when the user does not have many previous posts, recommendations need to take other information into account. We propose a multiagent system architecture where agents of the users consult other users' agents about possible privacy rules they can take into account.