q-Anon: practical anonymity for social networks

Abstract

This paper discusses why many of the common assumptions made in anonymity research cannot be applied to social network data. In particular, the concepts of ‘public’ and ‘private’ cannot be used to neatly distinguish certain types of social network data from others. It is proposed that social network data should be assumed public and treated private. An alternative anonymity model, q-Anon, is presented, which reconciles the paradox of social network data’s public/private nature. Finally, the feasibility of such an approach is evaluated suggesting that a social network site such as Facebook could practically implement an anonymous API using q-Anon. The paper concludes with a practical discussion of how q-Anon may affect different types of applications.