Privacy dynamics

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 capabilities 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 Privacy Dynamics, an adaptive architecture that learns privacy norms for different 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 relations. Our formal model comprises two main concepts, the group membership as a Social Identity (SI) map and privacy norms as a set of conflict rules. In our approach a privacy norm is specified in terms of the information objects that should be prevented from flowing between two conflicting social identity groups. We implement our formal model by using inductive logic programming (ILP), which automatically learns privacy norms. We evaluate the performance of our learning approach using synthesised data representing the sharing behaviour of social network users.

Norm-based Access Control

“…However, it did not consider important aspects including prudential norms, aging of privacy decisions, or agent's autonomy in choosing to follow norms. Calikli et al [9] employ a social identity map for relationships of users and a set of social identity conflict rules to learn the privacy norms for social networks. Mashayekhi et al [20] study norm emergence in traffic domain, where agents enter and leave and no known network structure among them exists.…”

Multiparty privacy in social media

“…MP is different from other collective approaches that focus on protecting just one individual, e.g.,[4],[6],[21].…”