Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles

1. http://lca.epfl.ch/privacy Preserving Privacy in Collaborative Filtering through Distributed Aggregation of Offline Profiles Reza Shokri Pedram Pedarsani George Theodorakopoulos Jean-Pierre Hubaux The 3rd ACM Conference on Recommender Systems, New York City, NY, USA, October 22-25, 2009

2. Privacy in Recommender Systems • Untrusted Server • Tracking users’ activities • Publishing Users’ Profiles • Re-identification attacks on anonymous datasets A. Narayanan and V. Shmatikov. Robust de-anonymization of large sparse datasets. In IEEE Symposium on Security and Privacy, 2008.

3. Problem Statement • Improving users privacy with minimum imposition of accuracy loss on the recommendations • Centralized recommender system • Contact between users • Distributed privacy preserving mechanism • Distributed aggregation of users’ profiles • Users hide the items they have actually rated through adding items rated by other users to their profile Proposed Solution

4. Outline • Profile Aggregation • Aggregation Methods • Evaluation

5. Profile Aggregation items 4 2 5 1 3 3 4 5 5 2 3 3 4 2 1 ratings Alice Bob • Each user gives a subset of his items to his contact peer • Thus, users profiles are aggregated after the contact

6. System Model Online profile contact synchronization Offline profile • Actual Profile: Set of items rated by a user • Offline Profile: Actual profile + aggregated items • Online Profile: The latest synchronized offline profile on the server

7. … … Actual profile of users … … Online profile of users Online Profiles vs. Actual Profiles

8. Aggregation Methods • How many items to aggregate? • Which items to aggregate? Similarity-based Aggregation (Similarity: The Pearson’s correlation coefficient) • Random Selection (SRS) • Minimum Rating Frequency (SMRF) (rating frequency: percentage of users that have rated an item) IMDB: 167,237 votes IMDB: 1,625 votes

9. Evaluation Metrics • Privacy Gain • Accuracy Loss

10. number of users actual profile of user ‘u’ rating frequency of item ‘i’ online profile of user ‘u’ Weight of items added by aggregation Weight of items in online profile Privacy Gain • Privacy: How difficult is for the server to guess the users’ actual profiles, having access to their online profiles Intuition: Structural difference of two graphs (online and actual) viewed as difference between correspondent edges R. Myers, R. C. Wilson, and E. R. Hancock. Bayesian graph edit distance. IEEE Trans. Pattern Anal. Mach. Intell., 22(6), 2000.

11. Accuracy Loss • The bipartite graph that contains actual ratings • The bipartite graph available to the server

12. Experiment • Simulation on randomly chosen profiles • From the Netflix prize dataset • 300 users • Average: 30000 ratings and 2500 items in each experiment • Memory-based CF: user-based • Testing set: 10% of the actual ratings of each user • Users select their contact peers at random • Aggregation methods • Union • SRS • SMRF

13. Privacy Gain • Similarity-based Random Selection (SRS) • Similarity-based Minimum Rating Frequency (SMRF)

14. Accuracy Loss • Similarity-based Random Selection (SRS) • Similarity-based Minimum Rating Frequency (SMRF)

15. Tradeoff between Privacy and Accuracy

16. Conclusion • A novel method for privacy preservation in collaborative filtering recommendation systems • Protection of users privacy against an untrusted server • Considerably improving users privacy with minimum effect on recommendations accuracy by aggregating users’ profiles based on their similarities • Proposed method can also be used on protecting privacy of users in published datasets

17. http://lca.epfl.ch/privacy Future Work • The evaluation of the mechanism can be improved by considering more realistic contact pattern between users, e.g., users friendship in a social network, or physical vicinity • We would like to evaluate the practical implication of the method on the maintenance of the profiles