To Join or Not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User Profiles By Elena Zheleva, Lise Getoor Presented by Ionut Trestian

Privacy is important ! (1)

Privacy is important ! (2)

Privacy is important ! (3) Sometimes it’s not the user who accidently exposes private information Groups, organizations that the users belong to might expose information accidentally or not

Contributions Identify novel social network attacks We show that such attacks can be carried out even with limited information We evaluate our attacks on real social network data (Flickr, Facebook, Dogster and BibSonomy) Discuss how our study affects anonymization of social networks

Types of attacks Attacks without links and groups {BASIC} – Pick the most probable attribute from public profiles – Simple, use as a baseline Privacy attacks using links Privacy attacks using groups Privacy attacks using links and groups

Privacy attacks using links Friend-aggregate model (AGG) – Pick the most probable attribute value from friends Collective classification model (CC) – Iterative classification Flat-link model (LINK) – Traditional classifiers, Bayes etc Blockmodeling attack (BLOCK) – Obtain blocks (clusters of users) and find where the user belongs

Privacy attacks using groups Groupmate-link model (CLIQUE) – Assume group members are friends Group-based classification model (GROUP) – Consider groups as features – Not all groups are relevant

Privacy attacks using links and groups Combine flat-link and group-based classification models into one LINK-GROUP Can use any traditional classifier

Experiments - Data Flickr -9,179 users from 55 countries (47,754 groups) Facebook – 1,598 users – political views Dogster – 2,632 dogs – 1,042 groups BibSonomy – 31,175 users + tags

Results (1) 50% private profiles

Results GROUP (2)

Results GROUP (3)

Results GROUP (4)

Results GROUP (5)

Results GROUP (6)

Results GROUP (7)

Discussion Joining heterogeneous groups preserves privacy better Display Group information only to friends Remove homogeneous groups

Thank you ! Questions ?