To Join or Not to Join: The Illusion of Privacy in Social Networks with Mixed Public and Private User

ProfilesBy 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 ?