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Paper by Elena Zhelea and Lise Getoor

To Join or Not to Join: The Illusion of Privacy in social Networks with Mixed Public and Private User Profiles. Paper by Elena Zhelea and Lise Getoor. Introduction. What do we want to find out about a “Private” profile? Sensitive information What Is Sensitive information ?

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Paper by Elena Zhelea and Lise Getoor

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  1. To Join or Not to Join: The Illusion of Privacy in social Networks with Mixed Public and Private User Profiles Paper by Elena Zhelea and Lise Getoor

  2. Introduction • What do we want to find out about a “Private” profile? • Sensitive information • What Is Sensitive information ? • What advertizing agencies and companies want to know • What you do not want others to find out

  3. How can we find out private information? • If a profile is really private how can you find out something? • What if it was not facebook? A completely anonymous profile? • Utilize what pubic info you have. • Using tactics that exploit friendship links • Exploiting group affiliations • Neither Facebook nor Flikr hide group members.

  4. BASIC model • Guess sensitive attribute based on distribution of known attributes. Don Ana ? Emma ? ? Bob Gia Sensitive Info =Favorite Colors Orange Blue Green Chris Fabio

  5. Sensitive-Attribute Inference Models • We assume the overall distribution of the sensitive attribute is either known or it can be found using public profiles. • We will consider the BASIC distribution to be the baseline attack. • A successful attack is one that with extra knowledge, has significantly higher accuracy.

  6. Our Model True Blue Lovers Espresso lovers Bob Don Fabio ? Gia Bob Chris Emma ? ? Don Ana ? Emma ? ? Bob Gia Sensitive Info = Favorite Color Orange Blue Green Chris Fabio

  7. “Tell me who your friends are, and I’ll tell you who are you” Link based Attacks • Friend-aggregate model (AGG) • Collective Classification model (CC) • Flat-link model (LINK)

  8. Friend-aggregate model (AGG) • Given my friends, what am I most likely? • Public-Sensitive attributes/Total Links Don Ana ? Emma ? ? Bob Gia Chris Fabio

  9. Collective Classification model (CC) • AGG, With re-evaluation Don Ana ? Emma ? ? Bob Gia Chris Fabio

  10. Flat-link model (LINK) • Flatten the data by considering adjacency matrix of the graph. • CLASSIFICATION!!! Don Ana ? Emma ? ? Bob Gia Chris Fabio

  11. Group Based Attacks • Groupmate-link model (CLIQUE) • Considers all people in a group as friends • Group-based classification model (GROUP) • Considers each group as a feature in a classifier

  12. Groupmate-link model (CLIQUE) True Blue Lovers Espresso lovers Bob Don • Consider everyone in a group, a friend • Then flatten to adjacency matrix • Use previous LINK methods after Fabio ? Gia Bob Chris Emma ? ?

  13. Group-based classification model (GROUP) True Blue Lovers Espresso Lovers Bob Don • Use groups as a feature set • Prune away less useful groups • Homogeneity = Entropy (h) • Size, smaller groups might be better. Fabio ? Gia Bob Chris Emma ? ?

  14. LINK-GROUP • Use friends and groups as features and then use traditional classifier

  15. Using Both –Groups and Links • LINK-GROUP • Uses the links and groups as features in a classifier model

  16. Facebook Data • Link based attacks • AGG, CC, BLOCK similar to baseline • LINK’s accuracy varied between 65.3% and 73.5% • Group based Attacks • 73.4% success in determining gender • Mixed-Model • 72.5%, no improvement, 57.8% or 1% better than BASIC on political views

  17. How good is this paper? • How good is their attack methods? • We can attack in more ways • Using image recognition • Using the names of people and “googling” • Also applies to doing the same to their friends • Search for key words in wall posts

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