1. 1 Privacy-Enhanced Collaborative Filtering Privacy-Enhanced Personalization workshop July 25, 2005, Edinburgh, Scotland Shlomo Berkovsky 1, Yaniv Eytani 1, Tsvi Kuflik 2, Francesco Ricci 3 1 Computer Science Department, University of Haifa, Israel 2 Management Information Systems Department, University of Haifa, Israel 3 ITC-irst, Trento, Italy This work is supported by the collaboration project between the University of Haifa and ITC/irst

2. 2 Outline l Collaborative Filtering (CF) l Distributed Privacy-Enhanced CF l Experimental Results l Open Questions

3. 3 Collaborative Filtering (CF) l Based on assumption that people with similar taste prefer similar items l 3 basic stages: –Similarity computation (Pearson correlation, Cosine, Mean-Squared Difference) –Neighborhood formation (K-Nearest Neighbors) –Personalized prediction generation (Weighted average of neighbors’ ratings)

4. 4 CF and Privacy l Service providers collect information about their users l Personalization raises the issue of privacy l Prior works: –[Canny] – P2P-based CF, users communities, encryption –[Polat&Du] – partitioning of CF data, data perturbation techniques

5. 5 Distributed Privacy-Enhanced CF l Combines the approaches of [Canny] and [Polat&Du] l Distributed and decentralized organization of users maintaining their personal profiles P 1 P 2 … … P j … … P m U1U2……Ui……UnU1U2……Ui……Un

6. 6 Recommendation Generation l A user sends his profile and requests a recommendation l Individual users independently decide whether to respond to the request l The responder locally computes and sends similarity and his prediction l The requesting user collects the responses, builds the neighborhood and generates the personalized prediction

7. 7 Privacy through Obfuscation l User profile might be revealed by malicious attacker through multiple requests l Privacy is increased by obfuscating parts of user profiles l Basic question: “What portion of user profile can be obfuscated while continuing to generate accurate recommendations?”

8. 8 Experimental Setting l Part of Jester dataset of jokes ratings (-10.. 10) l Dense dataset of 1024 users x 100 jokes l 3 obfuscation policies: –Default(x) – replace the ratings with x –Uniform – replace the ratings with random values chosen uniformly in the scope of ratings –Bell_curve – replace the ratings with random values chosen according to the distribution of real ratings in the dataset (bell curve distribution)

9. 9 Experimental Results

10. 10 Open Questions l Will these results be true for other datasets? –Sparse datasets, e.g. MovieLens –“Extreme” ratings, e.g. edges of the bell curve l Will our approach scale under an organized attack of multiple malicious users?

11. 11 Open Questions l Can the profile of the active user be also obfuscated to increase privacy? l Can just a portion of user profile be communicated to decrease communication costs and to improve scalability?

12. 12 Q & A Thank You!

13. 13 Question l What happens if we simply give a random recommendation?