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UnFriendly: Multi-Party Privacy Risks in Social Networks Kurt Thomas, Chris Grier, David M. Nicol.

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Presentation on theme: "UnFriendly: Multi-Party Privacy Risks in Social Networks Kurt Thomas, Chris Grier, David M. Nicol."— Presentation transcript:

1 unFriendly: Multi-Party Privacy Risks in Social Networks Kurt Thomas, Chris Grier, David M. Nicol

2 Problem Social networks propelled by personal content – Upload stories, photos; disclose relationships – Access control limited to owners Content can reference multiple parties – Distinct privacy requirements for each party – Currently, only one policy enforced Friends, family inadvertently leak sensitive information 2

3 Consequences One photo or message leaked may be harmless.. – Aggregate stories, friends, photos form a composite Can infer personal data from these public references – Weighted by perceived importance of relationships In practice, can predict personal attributes with up to 83% accuracy – Directly tied to amount, richness of exposed data – Independent of existing privacy controls 3

4 Solution Adapt privacy controls: – Grant users control over all personal references, regardless where it appears – Includes tags, mentions, links – Allow users to specify global privacy settings Prototype solution as a Facebook application – Satisfies privacy requirements of all users referenced – Determines mutually acceptable audience; restricts access to everyone else 4

5 Overview Existing privacy controls Sources of conflicting requirements Inferring personal details from leaks Inference performance Devising a solution Conclusion 5

6 Existing Controls EveryoneFriends of Friends Only Friends Friend List  Wall Posts  Personal Details  Photos, Videos  6

7 Privacy Conflict Social networks recognize only one owner – But data can pertain to multiple users – Each user has potentially distinct privacy requirement Privacy Conflict: – When two or more users disagree on data’s audience – Results in data exposed against a user’s will 7

8 Privacy Conflict – Friendships Privacy Requirement: Hide sensitive relationships Privacy Conflict: Alice reveals her friends Link between Alice-Bob revealed by Alice 8

9 Privacy Conflict – Wall Posts Privacy Requirement: Control audience of post Privacy Conflict: Anything posted to Alice’s wall is public Content written by Bob exposed by Alice 9 Bob > Alice: Just broke up with Carol..

10 Privacy Conflict – Tagging Privacy Requirement: Hide sensitive posts Privacy Conflict: Alice shares her posts Details about Bob exposed by Alice 10 Alice: Skipping work with @Bob!

11 Aggregating Leaked Data Threat model: – Adversary crawls entire social network – Collects all public references to a user; messages, friendships, tagged content – Feasible for search engines, marketers, political groups Exposure Set – All public information in conflict with a user’s privacy requirement 11

12 Inferring Personal Details Given exposure set, analyze whether leaks create an accurate composite of user Attempt to predict 8 values from exposure set: – Personal: Gender, religion, political view, relation status – Media: Favorite books, TV shows, movies, music Compare predictions to scenario where no privacy conflict exists 12

13 Inference Approaches Friendships: – Base predictions on attributes of friends – Users with liberal, Catholic friends who like Twilight tend to be… – Weight relationships on perceived importance; distinguish strong friends from acquaintances Frequency of communication Mutual friends; community – Feed vector of attributes, weights into multinomial logistic regression 13

14 Inference Approaches Wall Content: – Base prediction on content written by private user, posted to public walls – A user who talks about sports, girlfriends, and cars tends to be … – Treat content as bag of words, weight terms based on TF-IDF – Feed vector of words into multinomial logistic regression 14

15 Experiment Setup Analyze inference accuracy on 80,000 Facebook profiles – 40,000 profiles from 2 distinct networks – Collect all references to a user appearing in public profiles, walls, friend lists Simulate private profiles – Used values reported in public profile as ground truth – Compare prediction against ground truth 15

16 Frequency Data is Exposed 16 StatisticNetwork ANetwork B Profiles in data set42,79640,544 Fraction of profiles public44%35% Avg. # relationships per profile in exposure set 4223 Avg. # wall posts per profile in exposure set 5343

17 Prediction Accuracy 17

18 More Conflicts, Better Accuracy 18

19 Improving Privacy Privacy must extend beyond single-owner model – Tags, links, mentions can reference multiple users – Rely on these existing features to distinguish who is at risk Allow each user to specify global privacy policy Enforce policy on all personal content, regardless page it appears 19

20 Enforcing Multi-Party Privacy 20 Alice: Looks like @Bob and @Carol are done for! Individual PoliciesU1U2U3U4U5U6 Alice  Bob  Carol  Mutual Policy 

21 Limitations In absence of mutual friends, safe set of viewers tends towards empty set Assume friends will consent to not sharing with wider audience Content must be tagged; no other way to distinguish privacy-affected parties Censorship; prevents negative speech 21

22 Conclusion Privacy goes beyond one person’s expectations – All parties affected must have a say – Existing model lacks multi-party support References to other users are common – Outside their control Aggregate exposed data contains sensitive features – Predictions will only get better By adopting multi-party privacy, can return control back to users 22

23 Questions? 23

24 Correlated Features Among Friends 24

25 Importance of Mutual Friends 25

26 Importance of Frequent Communication 26

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