Privacy Dynamics: Learning Privacy Norms for Social Software

Abstract

Privacy violations in online social networks (OSNs) often arise as a result of users sharing information with unintended audiences. One reason for this is that, although OSN capa- bilities for creating and managing social groups can make it easier to be selective about recipients of a given post, they do not provide enough guidance to the users to make informed sharing decisions. In this paper we present Pri- vacy Dynamics, an adaptive architecture that learns privacy norms for di erent audience groups based on users' sharing behaviours. Our architecture is underpinned by a formal model inspired by social identity theory, a social psychology framework for analysing group processes and intergroup re- lations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of con ict rules. In our approach a privacy norm is speci ed in terms of the information objects that should be prevented from owing between two con icting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automati- cally learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.