ZHAO Feng, XIONG Yan, LIANG Xiao, GONG Xudong, LU Qiwei. Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic[J]. Chinese Journal of Electronics, 2016, 25(1): 20-25. doi: 10.1049/cje.2016.01.004
Citation: ZHAO Feng, XIONG Yan, LIANG Xiao, GONG Xudong, LU Qiwei. Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic[J]. Chinese Journal of Electronics, 2016, 25(1): 20-25. doi: 10.1049/cje.2016.01.004

Privacy-Preserving Collaborative Filtering Based on Time-Drifting Characteristic

doi: 10.1049/cje.2016.01.004
Funds:  The research is supported by National Natural Science Foundation of China (No.61202404, No.61170233, No.61232018, No.61272472, No.61272317, No.61300170), the Fundamental Research Funds for the Central Universities (No.WK0110000036) and University Provincial Natural Science Foundation of Anhui Province (No.KJ2013A040).
  • Received Date: 2014-01-15
  • Rev Recd Date: 2014-03-05
  • Publish Date: 2016-01-10
  • Recommendation has become increasingly important because of the information overload. Collaborative filtering (CF) technique, as the most popular recommendation method, utilizes the historical preferences of users to predict their future interests on other items. However, CF technique requires collecting users' rating information, which may lead to the disclosure of privacy. We propose a new randomized perturbation approach Time-drifting privacy-preserving collaborative filtering (TPPCF) to well balance privacy of users and accuracy of recommendation. Since users' recent ratings can better represent their interests and preferences, we incorporate a varying weight into the approach. Specifically, we assign higher weights to more recent ratings both when computing user similarity and perturbing users' ratings. To further improve the efficiency, we cluster the users into several groups to reduce computation cost. We demonstrate the effectiveness and efficiency of our method through experiments on MovieLens dataset, which shows TPPCF can achieve higher privacy while generating more accurate recommendation.
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  • Chih-Cheng Chang, et al., “Towards publishing recommendation data with predictive anonymization”, Proceedings of the 5th ACM Symposium on Information, Computer and Communications Security, Beijing, China, pp.24-35, 2010.
    C-LA Hsieh, Justin Zhan, Deniel Zeng and Feiyue Wang, “Preserving privacy in joining recommender systems”, International Conference on Information Security and Assurance, Busan, Korea, pp.561-566, 2008.
    Zekeriya Erkin, et al., “Efficiently computing private recommendations”, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Prague, Czechoslovakia, pp.5864-5867, 2011.
    John Canny, “Collaborative filtering with privacy”, 2002 IEEE Symposium on Security and Privacy, Oakland, California, USA, pp.45-57, 2002.
    H. Polat and Wenliang Du, “Privacy-preserving collaborative filtering using randomized perturbation techniques”, Third IEEE International Conference on Data Mining, Melbourne, Florida, USA, pp.625-628, 2003.
    S. Zhang, et al., “A privacy-preserving collaborative filtering scheme with two-way communication”, Proceedings of the 7th ACM Conference on Electronic Commerce, Vancouver, British Columbia, Canada, pp.316-323, 2006.
    Reza Shokri, et al., “Preserving privacy in collaborative filtering through distributed aggregation of offline profiles”, Proceedings of the Third ACM Conference on Recommender Systems, New York, NY, USA, pp.157-164, 2009.
    Z. Huang, W. Du and B. Chen, “Deriving private information from randomized data”, Proceedings of the 2005 ACM SIGMOD International Conference on Management of Data, Baltimore, Maryland, USA, pp.37-48, 2005.
    Arvind Narayanan, et al., How to break anonymity of the netflix prize dataset, arXiv preprint cs/0610105, 2006.
    Qiwei Lu, Yan Xiong, Xudong Gong and Wenchao Huang, “Secure collaborative outsourced data mining with multi-owner in cloud computing”, 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom), Liverpool, England, pp.100-108, 2012.
    Chia-Lung Hsieh, “Toward better recommender system by collaborative computation with privacy preserved”, 2011 IEEE/IPSJ 11th International Symposium on Applications and the Internet (SAINT), Munich, Bavaria, pp.246-249, 2011.
    S.J. Gong and G.H. Cheng, “Mining user interest change for improving collaborative filtering”, Second International Symposium on Intelligent Information Technology Application, Shanghai, China, Vol.3, pp.24-27, 2008.
    Y. Koren, “Collaborative filtering with temporal dynamics”, Communications of the ACM, Vol.53, No.4, pp.89-97, 2010.
    J.S. Breese, D. Heckerman and C. Kadie, “Empirical analysis of predictive algorithms for collaborative filtering”, Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, San Francisco, CA, USA, pp.43-52, 1998.
    B. Sarwar, G. Karypis, J. Konstan and J. Riedl, “Item-based collaborative filtering recommendation algorithms”, Proceedings of the 10th International Conference on World Wide Web, Hong Kong, China, pp.285-295, 2001.
    G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, IEEE Transactions on Knowledge and Data Engineering, Vol.17, No.6, pp.734-749, 2005.
    J.L. Herlocker, et al., “An algorithmic framework for performing collaborative filtering”, Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, California, Berkekey, pp.230- 237, 1999.
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