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Perturbation Based Privacy Preserving Slope One Predictors for Collaborative Filtering

  • Anirban Basu
  • Jaideep Vaidya
  • Hiroaki Kikuchi
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 374)

Abstract

The prediction of the rating that a user is likely to give to an item, can be derived from the ratings of other items given by other users, through collaborative filtering (CF). However, CF raises concerns about the privacy of the individual user’s rating data. To deal with this, several privacy-preserving CF schemes have been proposed. However, they are all limited either in terms of efficiency or privacy when deployed on the cloud. Due to its simplicity, Lemire and MacLachlan’s weighted Slope One predictor is very well suited to the cloud. Our key insight is that, the Slope One predictor, being an invertible affine transformation, is robust to certain types of noise. We exploit this fact to propose a random perturbation based privacy preserving collaborative filtering scheme. Our evaluation shows that the proposed scheme is both efficient and preserves privacy.

Keywords

Singular Value Decomposition Random Noise Collaborative Filter Homomorphic Encryption Query Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Schafer, J.B., Konstan, J., Riedi, J.: Recommender systems in e-commerce. In: Proceedings of the 1st ACM Conference on Electronic Commerce, pp. 158–166. ACM Press, New York (1999)CrossRefGoogle Scholar
  2. 2.
    Lemire, D., Maclachlan, A.: Slope one predictors for online rating-based collaborative filtering. Society for Industrial Mathematics (2005)Google Scholar
  3. 3.
    Canny, J.: Collaborative filtering with privacy. In: Proceedings 2002 IEEE Symposium on Security and Privacy, pp. 45–57 (2002)Google Scholar
  4. 4.
    Kaleli, C., Polat, H.: P2P collaborative filtering with privacy. Turkish Journal of Electric Electrical Engineering and Computer Sciences 8(1), 101–116 (2010)Google Scholar
  5. 5.
    Han, S., Ng, W.K., Yu, P.S.: Privacy-Preserving Singular Value Decomposition. In: IEEE 25th International Conference on Data Engineering, pp. 1267–1270 (2009)Google Scholar
  6. 6.
    Polat, H., Du, W.: SVD-based collaborative filtering with privacy. In: Proceedings of the 20th ACM Symposium on Applied Computing (2005)Google Scholar
  7. 7.
    Aggarwal, C.C., Yu, P.S.: A General Survey of Privacy-Preserving Data Mining Models and Algorithms. In: Privacy-Preserving Data Mining, ch. 2, pp. 11–52. Springer (2008), http://www.springerlink.com/index/u4419h332616un75.pdf
  8. 8.
    Basu, A., Kikuchi, H., Vaidya, J.: Privacy-preserving weighted slope one predictor for item-based collaborative filtering. In: Proceedings of the International Workshop on Trust and Privacy in Distributed Information Processing (workshop at the IFIPTM 2011), Copenhagen, Denmark (2011)Google Scholar
  9. 9.
    Basu, A., Vaidya, J., Kikuchi, H.: Efficient privacy-preserving collaborative filtering based on the weighted Slope One predictor. Journal of Internet Services and Information Security 1(4) (2011)Google Scholar
  10. 10.
    Basu, A., Vaidya, J., Kikuchi, H., Dimitrakos, T.: Privacy-preserving collaborative filtering for the cloud. In: Proceedings of the 3rd IEEE International Conference on Cloud Computing Technology and Science (Cloudcom), Athens, Greece (2011)Google Scholar
  11. 11.
    Agrawal, R., Srikant, R.: Privacy-preserving data mining. ACM Sigmod Record 29, 439–450 (2000)CrossRefGoogle Scholar
  12. 12.
    Agrawal, D., Aggarwal, C.C.: On the design and quantification of privacy preserving data mining algorithms. In: Proceedings of the Twentieth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 247–255. ACM (2001)Google Scholar
  13. 13.
    Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. Information Systems 29(4), 343–364 (2004)CrossRefGoogle Scholar
  14. 14.
    Evfimievski, A.: Randomization in privacy preserving data mining. ACM SIGKDD Explorations Newsletter 4(2), 43–48 (2002)CrossRefGoogle Scholar
  15. 15.
    Villejoubert, G., Mandel, D.: The inverse fallacy: An account of deviations from Bayes’s theorem and the additivity principle. Memory & Cognition 30(2), 171–178 (2002)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2012

Authors and Affiliations

  • Anirban Basu
    • 1
  • Jaideep Vaidya
    • 2
  • Hiroaki Kikuchi
    • 1
  1. 1.Graduate School of EngineeringTokai UniversityMinato-kuJapan
  2. 2.MSIS DepartmentRutgers, The State University of New JerseyNewarkUSA

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