A privacy-preserving collaborative filtering scheme with two-way communication

Proceedings of the ACM Conference on Electronic Commerce

Volumn 2006, Issue , 2006, Pages 316-323

  Zhang, Sheng ^a   Ford, James ^a   Makedon, Fillia ^a  

^a Dartmouth Department of Computer Science_Dartmouth College   (United States)

Author keywords

Collaborative filtering; Privacy; Randomization; Two way communication

Indexed keywords

COMMUNICATION SYSTEMS; DATA ACQUISITION; PERTURBATION TECHNIQUES; SERVERS;

COLLABORATIVE FILTERING; RANDOMIZATION; TWO-WAY COMMUNICATION;

COMPUTER SUPPORTED COOPERATIVE WORK;

EID: 33748679129     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1134707.1134742     Document Type: Conference Paper

References (22)

1. D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proc. of the 20th ACM PODS, pages 247-255, 2001.
2. R. Agrawal and R. Srikant. Privacy preserving data mining. In Proc. of the ACM SIGMOD, pages 439-450, 2000.
3. M. Brand. Incremental singular value decomposition of uncertain data with missing values. In Proc. of European Conf. on Computer Vision, pages 707-720, 2002.
4. J. R. Bunch and C. P. Nielsen. Updating the singular value decomposition. Numerische Mathematik, 31(2):111-129, 1978.
5. J. Canny. Collaborative filtering with privacy. In Proc. of the IEEE Symp. on Security and Privacy, pages 45-57, 2002.
6. J. Canny. Collaborative filtering with privacy via factor analysis. In Proc. of the 25th ACM SIGIR, pages 238-245, 2002.
7. L. F. Cranor, J. Reagle, and M. S. Ackerman. Beyond concern: Understanding net users' attitudes. Technical report, AT&T Research, available at http://www.research.att.com/projects/privacystudy/, 1999.
8. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. J. of the Royal Statistical Society, series B, 39(1):1-38, 1977.
9. W. Du and M. J. Atallah. Privacy-preserving cooperative statistical analysis. In Proc. of the 17th Annual Computer Security Applications Conference, pages 102-112, 2001.
10. A. Evfimievski, J. Gehrke, and R. Srikant. Limiting privacy breaches in privacy preserving data mining. In Proc. of the 22nd ACM PODS, pages 211-222, 2003.
11. A. Evfimievski, R. Srikant, R. Agrawal, and J. Gehrke. Privacy preserving mining of association rules. In Proc. of the 8th ACM SIGKDD, pages 217-228, 2002.
12. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proc. of the 22nd ACM SIGIR, pages 230-237, 1999.
13. J. L. Herlocker, J. A. Konstan, L. G. Terveen, and J. T. Riedl. Evaluating collaborative filtering recommender systems. ACM Tran. on Information Systems, 22(1):5-53, 2004.
14. Z. Huang, W. Du, and B. Chen. Deriving private information from randomized data. In Proc. of the ACM SIGMOD, pages 37-48, 2005.
15. H. Kargupta, S. Datta, Q. Wang, and K. Sivakumar. On the privacy preserving properties of random data perturbation techniques. In Proc. of the IEEE Int. Conf. on Data Mining, pages 99-106, 2003.
16. H. Polat and W. Du. Privacy-preserving collaborative filtering using randomized perturbation techniques. In Proc. of the 3rd IEEE Int. Conf. on Data Mining, pages 625-628, 2003.
17. H. Polat and W. Du. SVD-based collaborative filtering with privacy. In Proc. of the 20th ACM Symp. on Applied Computing, pages 791-795, 2005.
18. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Reidl. Item-based collaborative filtering recommendation algorithms. In Proc. of the 10th WWW, pages 285-295, 2001.
19. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. Riedl. Application of dimensionality reduction in recommender systems-a case study. In Proc. of the ACM WebKDD Workshop, 2000.
20. N. Zhang, S. Wang, and W. Zhao. A new scheme on privacy preserving association rule mining. In Proc. of the 8th European Conf. on Principles and Practice of Knowledge Discovery in Databases, pages 484-495, 2004.
21. N. Zhang, S. Wang, and W. Zhao. A new scheme on privacy-preserving data classification. In Proc. of the 11th ACM SIGKDD, pages 374-383, 2005.
22. S. Zhang, J. Ford, and F. Makedon. Deriving private information from randomly perturbed ratings. To appear in SIAM-Data Mining Conf. 2006.