P2P collaborative filtering with privacy

With the evolution of the Internet and e-commerce, collaborative filtering (CF) and privacy-preserving collaborative filtering (PPCF) have become popular. The goal in CF is to generate predictions with decent accuracy, efficiently. The main issue in PPCF, however, is achieving such a goal while preserving users' privacy. Many implementations of CF and PPCF techniques proposed so far are centralized. In centralized systems, data is collected and stored by a central server for CF purposes. Centralized storage poses several hazards to users because the central server controls users' data. In this work, we investigate how to produce naïve Bayesian classifier (NBC)-based recommendations while preserving users' privacy without using a central server. In a community of people, users might create a peer-to-peer (P2P) network. Through P2P network, users can communicate with each other and exchange data to produce predictions. We share the workload of prediction process and offer referrals efficiently using P2P network. We propose privacy-preserving schemes and analyze them in terms of accuracy, privacy, and efficiency. Our real data-based results show that our schemes offer accurate NBC-based predictions with privacy eliminating central server.

Anahtar Kelimeler:

Privacy, P2P, collaborative filtering, naïve bayesian classifier, accuracy.

P2P collaborative filtering with privacy

Keywords:

Privacy, P2P, collaborative filtering, naïve bayesian classifier, accuracy.,

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J.L. Herlocke, J.A. Konstan, J.T. Riedl, “Explaining Collaborative Filtering Recommendations”, Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, pp. 241–250, USA, 2000.
D. Goldberg, D. Nichols, and B.M. Oki, “Using collaborative Şltering to weave an Information Tapestry,” Commu- nications of the ACM, 35, 12, pp. 61–70, 1992.
D.M. Pennock, E. Horvitz, S. Lawrence, C.L. Giles, “Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and Model-Based Approach,” Proceedings of the 16th Conference on Uncertainty in ArtiŞcial Intelligence, pp. 473–480, Stanford, CA, USA, July 2000.
M. Grcar, “User proŞling: collaborative Şltering”, Proceedings of the SIKDD 2004 at Multiconference IS, Ljubljana, Slovenia, 2004.
J. S. Breese, D. Heckerman, C. Kadie, “Empirical analysis of predictive algorithms for collaborative Şltering,” Proceedings of the 14th Conference on Uncertainty in ArtiŞcial Intelligence, pp. 43–52, USA, 1998.
B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl,” Item-based collaborative Şltering recommendation algorithms,” Proceedings of the 10th International World Wide Web Conference, pp. 285–295, Hong Kong, 2001.
K. Miyahara, M. J. Pazzani, “Improvement of collaborative Filtering with the simple Bayesian classiŞer”, IPSJ Journal, Vol. 43(11), 2002.
C. Basu, H. Hirsh, W. W. Cohen, “Recommendation as classiŞcation: Using social and content-based information in recommendation,” Proceedings of the Recommender System Workshop’ 98, pp. 714–720, 1998.
S. Meruge, J. Ghosh, “Privacy-preserving distributed clustering using generative models”, Proceedings of the 3rd IEEE International Conference on Data Mining, USA, pp. 211–218, 2003.
L. H. Ungar, D. P. Foster, “Clustering methods for collaborative Şltering,” Proceedings of the Workshop on Recommendation Systems at the 15th National Conference on ArtiŞcial Intelligence, USA, 1998.
B. M. Sarwar, G. Karypis, J. A. Konstan, J. Riedl, “Analysis of recommendation algorithms for e-commerce”, Proceedings of the 2nd ACM conference on Electronic commerce, pp. 158–163, Minnesota, USA, 2000.
J. Canny, “Collaborative Filtering with privacy via factor analysis”, Proceedings of the 25thACM SIGIR’02, pp. 238–245, Finland, 2002.
A. F. Westin, “Freebies and privacy: What net users think.” Technical report Opinion Research Corporation, 1999. [14] L. F. Cranor, “‘I didn’t buy it for myself’ privacy and e-commerce personalization,” Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society, pp. 111–117, USA, 2003.
L. F. Cranor, J. Reagle, M. S. Ackerman, “Beyond concern: Understanding net users’ attitudes about online privacy,” Technical report, AT T Labs-Research, 1999.
A. Tveit, “Peer-to-Peer Based Recommendations for Mobile Commerce,” Proceedings of the International Workshop on Mobile Commerce, pp. 26–29, Italy, 2001.
T. Olsson, “Decentralized Social Filtering based on Trust,” Proceedings of the National Conference of the American Association of ArtiŞcial Intelligence Recommender Systems Workshops, Madison, WI, 1998.
B. M. Sarwar, J. A. Konstan, J. Riedl, “Distributed Recommender Systems: New opportunities for Internet Com- merce,” Internet Commerce and Software Agents: Cases, Technologies and Opportunities, Idea Group Publishers, 2001.
P. Han, B. Xie, F. Yang, R. Shen, “A Scalable P2P Recommender System Based on Distributed Collaborative Filtering,” Expert Systems with Applications, Vol. 27, pp. 203–210, 2004.
S. Berkovsky, Y. Eytani, T. Kuﬂik, F. Ricci, “Collaborative Filtering over Distributed Environment,” Proceed- ings of the Workshop on Decentralized, Agent-Based and Social Approaches to User Modeling (DASUM), at the International Conference on User Modeling (UM), pp. 1–11, UK, 2005.
H. Polat, W. Du, “Privacy-preserving collaborative Şltering,” International Journal of Electronic Commerce, 9 (4), pp. 9–36, 2005.
H. Polat, W. Du, “Achieving private recommendations using randomized response techniques”, Lecture Notes in Computer Science, 3918, pp. 637–646, 2006.
C. Kaleli, H. Polat, “Providing Private Recommendations Using Na¨ıve Bayesian ClassiŞer,” Advances in Intelligent Web Mastering, Vol. 43, pp. 168–173, 2007.
J. Canny, “Collaborative Şltering with privacy”, Proceedings of the IEEE Symposium on Security and Privacy (IEEE S&P’02), pp. 45–57, 2002.
S. Berkovsky, Y. Eytani, T. Kuﬂik, F. Ricci, “Privacy-Enhanced Collaborative Filtering,” Proceedings of the Workshop on Privacy-Enhanced Personalization (PEP), at the International Conference on User Modeling (UM), pp. 75–83, UK, 2005.
N. Lathia, S. Hailes, L. Capra, “Private Distributed Collaborative Filtering Using Estimated Concordance Mea- sures,” Proceedings of RecSys’07, USA, 2007.
S. L. Warner, “Randomized response: A survey technique for eliminating evasive answer bias,” Journal of the American Statistical Association, Vol. 60(309), pp. 63–69, 1965.
J. K. Kim, H. K. Kim, Y. H. Cho, “A user-oriented contents recommendation system in peer-to-peer architecture,” Journal of Expert Systems with Applications, 34, pp.. 300-312, 2008.
D. Gupta, M. Digiovanni, H. Narita, K. Goldberg, “Jester 2.0: A new linear-time collaborative Şltering algorithm applied to jokes,” Proceedings of the Workshop on Recommender Systems: Algorithms and Evaluation, 22nd Annual International ACM SIGIR Conference, Berkeley, CA, USA, August 1999.
P. McJonese, “EachMovie collaborative Şltering data set”, DEC Systems Research Center, 1997.