Abstract
Recommender systems, which provide users with suggestions for selecting items that is of potential interest to them, are widely used to assist mobile users in reducing information overload and making better choice quickly in their daily life. Social recommender systems, which have the potential to mitigate the new user cold-start problem, utilize social relationships as an extra source of information. As recommendation results depend on users’ individual data, privacy breaches may occur. Although several differentially private social recommender systems have been proposed, their application scopes or protection strengths are limited. In this paper, we propose a differentially private social recommender system for mobile users named \(\mathcal D\!P\!S\!R\) to block curious users from inferring the existence of someone else’s numeric rating or social relationship. Empirical evaluations on two real-world datasets are conducted, and the results show that \(\mathcal D\!P\!S\!R\) can balance the utility of recommendations with the privacy of users’ data in both normal and cold-start test view.
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References
Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, New York, USA, October 2016
Calandrino, J.A., Kilzer, A., Narayanan, A., Felten, E.W., Shmatikov, V.: “You might also like:” privacy risks of collaborative filtering. In: 2011 IEEE Symposium on Security and Privacy, Berkeley, USA, July 2011
Dang, Q.V., Ignat, C.L.: dTrust: a deep learning approach for social recommendation. In: The 3rd IEEE International Conference on Collaboration and Internet Computing, San Jose, USA, October 2017
Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_14
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Found. Trends Theoret. Comput. Sci. 9(3–4), 211–407 (2014)
Guo, G., Zhang, J., Yorke-Smith, N.: TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, USA, January 2015
Guo, T., Luo, J., Dong, K., Yang, M.: Differentially private graph-link analysis based social recommendation. Inf. Sci. 463–464, 214–226 (2018)
Hou, Y., Holder, L.B.: Deep learning approach to link weight prediction. In: 2017 International Joint Conference on Neural Networks, Anchorage, USA, May 2017
Jorgensen, Z., Yu, T.: A privacy-preserving framework for personalized, social recommendations. In: Proceedings of the 17th International Conference on Extending Database Technology, Athens, Greece, March 2014
King, I., Lyu, M.R., Ma, H.: Introduction to social recommendation. In: Proceedings of the 19th International Conference on World Wide Web, Raleigh, USA, April 2010
Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization. In: Proceedings of the fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China, February 2011
Machanavajjhala, A., Korolova, A., Sarma, A.D.: Personalized social recommendations: accurate or private. Proc. VLDB Endow. 4(7), 440–450 (2011)
Meng, X., et al.: Personalized privacy-preserving social recommendation. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence, New Orleans, USA, February 2018
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Advances in Neural Information Processing Systems, Vancouver, Canada, December 2008
Acknowledgement
This work was supported by the National Key Research and Development Program of China under Grant 2017YFB0802202, and by the Natural Science Foundation of China (NSFC) under Grants 61702474 and 61871362.
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Zhou, X., Wei, L., Niu, Y., Zhang, C., Fang, Y. (2019). DPSR: A Differentially Private Social Recommender System for Mobile Users. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_54
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DOI: https://doi.org/10.1007/978-3-030-23597-0_54
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