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SVD-based collaborative filtering with privacy

Published: 13 March 2005 Publication History

Abstract

Collaborative filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. Such techniques recommend products to customers using similar users' preference data. The performance of CF systems degrades with increasing number of customers and products. To reduce the dimensionality of filtering databases and to improve the performance, Singular Value Decomposition (SVD) is applied for CF. Although filtering systems are widely used by E-commerce sites, they fail to protect users' privacy. Since many users might decide to give false information because of privacy concerns, collecting high quality data from customers is not an easy task. CF systems using these data might produce inaccurate recommendations. In this paper, we discuss SVD-based CF with privacy. To protect users' privacy while still providing recommendations with decent accuracy, we propose a randomized perturbation-based scheme.

References

[1]
Anonymizer.com: http://www.anonymizer.com.
[2]
D. Agrawal and C. C. Aggarwal. On the design and quantification of privacy preserving data mining algorithms. In Proceedings of the 20th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database System, Santa Barbara, CA, May 21-23 2001.
[3]
R. Agrawal and R. Srikant. Privacy-preserving data mining. In Proceedings of the 2000 ACM SIGMOD on Management of Data, pages 439--450, Dallas, TX, May 15-18 2000.
[4]
D. Billsus and M. J. Pazzani. Learning collaborative information fiters. In Proceedings of the 1998 Workshop on Recommender Systems, August 1998.
[5]
J. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence, pages 43--52, Madison, WI, July 1998.
[6]
J. Canny. Collaborative filtering with privacy. In IEEE Symposium on Security and Privacy, pages 45--57, Oakland, CA, May 2002.
[7]
J. Canny. Collaborative filtering with privacy via factor analysis. In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 238--245, Tampere, Finland, August 2002.
[8]
L. F. Cranor, J. Reagle, and M. S. Ackerman. Beyond concern: Understanding net users' attitudes about online privacy. Technical report, AT&T Labs-Research, April 1999. Available from http://www.research.att.com /library/trs/TRs/99/99.4.3/report.htm.
[9]
D. Gupta, M. Digiovanni, H. Narita, and K. Goldberg. Jester 2.0: A new linear-time collaborative filtering algorithm applied to jokes. In Workshop on Recommender Systems Algorithms and Evaluation, 22nd International Conference on Research and Development in Information Retrieval, Berkeley, CA, 2000.
[10]
J. L. Herlocker, J. A. Konstan, A. Borchers, and J. T. Riedl. An algorithmic framework for performing collaborative filtering. In Proceedings of the 1999 Conference on Research and Development in Information Retrieval, August 1999.
[11]
H. Polat and W. Du. Privacy-preserving collaborative filtering using randomized perturbation techniques. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM'03), Melbourne, FL, November 19-22 2003.
[12]
M. K. Reiter and A. D. Rubin. Crowds: Anonymity for Web transaction. ACM Transactions on Information and System Security, 1(1):Pages 66--92, 1998.
[13]
B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedl. Application of dimensionality reduction in recommender system-a case study. In ACM WebKDD 2000 Web Mining for E-commerce Workshop, 2000.
[14]
A. F. Westin. Freebies and privacy. Technical report, Opinion Research Corporation, July 1999. Availabe from http://www.privacyexchange.org/iss/surveys/sr990714.html.

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cover image ACM Conferences
SAC '05: Proceedings of the 2005 ACM symposium on Applied computing
March 2005
1814 pages
ISBN:1581139640
DOI:10.1145/1066677
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 13 March 2005

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Author Tags

  1. SVD
  2. collaborative filtering
  3. privacy
  4. randomization

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SAC05
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SAC05: The 2005 ACM Symposium on Applied Computing
March 13 - 17, 2005
New Mexico, Santa Fe

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Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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  • (2024)Federated Supervised Principal Component AnalysisIEEE Transactions on Information Forensics and Security10.1109/TIFS.2023.332698119(646-660)Online publication date: 2024
  • (2024)Federated Conversational Recommender SystemsAdvances in Information Retrieval10.1007/978-3-031-56069-9_4(50-65)Online publication date: 23-Mar-2024
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  • (2023)Efficient, secure and verifiable outsourcing scheme for SVD-based collaborative filtering recommender systemFuture Generation Computer Systems10.1016/j.future.2023.07.042149(445-454)Online publication date: Dec-2023
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  • (2023)Prison Break: From Proprietary Data Sources to SSI Verifiable CredentialsAdvanced Information Networking and Applications10.1007/978-3-031-28451-9_31(355-366)Online publication date: 15-Mar-2023
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