Abstract
Collaborative filtering (CF) systems are receiving increasing attention. Data collected from users is needed for CF; however, many users do not feel comfortable to disclose data due to privacy risks. They sometimes refuse to provide information or might decide to give false data. By introducing privacy measures, it is more likely to increase users’ confidence to contribute their data and to provide more truthful data. In this paper, we investigate achieving referrals using item-based algorithms on binary ratings without greatly exposing users’ privacy. We propose to use randomized response techniques (RRT) to perturb users’ data. We conduct experiments to evaluate the accuracy of our scheme and to show how different parameters affect our results using real data sets.
This work was supported by Grants ISS-0219560 and ISS-0312366 from the United States National Science Foundation.
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References
Canny, J.: Collaborative filtering with privacy. In: Proceedings of the IEEE Symposium on Security and Privacy, Oakland, CA, USA, May 2002, pp. 45–57 (2002)
Canny, J.: Collaborative filtering with privacy via factor analysis. In: Proceedings of the 25th ACM SIGIR Conference, Tampere, Finland, August 2002, pp. 238–245 (2002)
Cranor, L.F.: I didn’t buy it for myself’ privacy and ecommerce personalization. In: Proceedings of the 2003 ACM Workshop on Privacy in the Electronic Society, Washington, DC, USA, pp. 111–117 (2003)
Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proceedings of the 9th International,
Gupta, D., Digiovanni, M., Narita, H., Goldberg, K.: Jester 2.0: A new linear-time collaborative filtering algorithm applied to jokes. In: Proceedings of the Workshop on Recommender Systems, ACM SIGIR 1999, Berkeley, CA, USA (August 1999)
Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.T.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference, Berkeley, CA, USA (August 1999)
Mild, A., Reutterer, T.: Collaborative filtering methods for binary market basket data analysis. In: Liu, J., Yuen, P.C., Li, C.-H., Ng, J., Ishida, T. (eds.) AMT 2001. LNCS, vol. 2252, pp. 302–313. Springer, Heidelberg (2002)
Polat, H., Du, W.: Privacy-preserving collaborative filtering. International Journal of Electronic Commerce 9(4), 9–36 (2005)
Polat, H., Du., W.: Privacy-preserving top-N recommendation on horizontally partitioned data. In: Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI 2005), Paris, France, September 19–22 (2005)
Rizvi, S.J., Haritsa, J.R.: Maintaining data privacy in association rule mining. In: Proceedings of the 28th VLDB Conference, Hong Kong, China (2002)
Sarwar, B.M., Karypis, G., Konstan, J.A., Reidl, J.T.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International World Wide Web Conference (WWW10), Hong Kong, May 2001, pp. 285–295 (2001)
Warner, S.L.: Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60(309), 63–69 (1965)
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Polat, H., Du, W. (2006). Achieving Private Recommendations Using Randomized Response Techniques. In: Ng, WK., Kitsuregawa, M., Li, J., Chang, K. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2006. Lecture Notes in Computer Science(), vol 3918. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11731139_73
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DOI: https://doi.org/10.1007/11731139_73
Publisher Name: Springer, Berlin, Heidelberg
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