Abstract
In the past decades, the ever-increasing popularity of the Internet has led to an explosive growth of information, which has consequently led to the emergence of recommendation systems. A series of encryption measures were adopted in the current recommendation systems in the cloud to protect users’ privacy security. However, there are many other privacy attacks in this recommendation system of the cloud-based device. Therefore, this paper studies the encryption interference of setting differential privacy protection mechanism for user data in user’s private devices based on untrusted servers. A dynamic privacy budget allocation method is proposed based on localized differential privacy protection technology and specific scenes recommended by movies.
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References
Chaudhuri, K., Sarwate, A., Sinha, K.: Near-optimal differentially private principal components. In: NIPS, pp. 989–997 (2012)
Chaudhuri, K., Vinterbo, S.A.: A stability-based validation procedure for differentially private machine learning. In: NIPS, pp. 2652–2660 (2013)
Fung, B.C.M., Wang, K., Chen, R., Yu, P.S.: Privacy-preserving data publishing: a survey of recent developments. ACM Comput. Surv. 42(4), 14:1–14:53 (2010)
Thakurta, A.G., Smith, A.: (Nearly) optimal algorithms for private online learning in full-information and bandit settings. In: NIPS, pp. 2733–2741 (2013)
Hardt, M., Ligett, K., Mcsherry, F.: A simple and practical algorithm for differentially private data release. In: NIPS, pp. 2339–2347 (2012)
McSherry, F., Mironov, I.: Differentially private recommender systems: building privacy into the net. In: KDD, pp. 627–636 (2009)
Ye, Q., Meng, X., Zhu, M., et al.: A review of localized differential privacy. J. Softw. 29(07), 159–183 (2018)
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: privacy via distributed noise generation. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 486–503. Springer, Heidelberg (2006). https://doi.org/10.1007/11761679_29
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., Mcsherry, F., Talwar, K.: The price of privacy and the limits of LP decoding. ACM Symposium on Theory of Computing. ACM (2007)
Vágvölgyi, S.: Descendants of a recognizable tree language for prefix constrained linear monadic term rewriting with position cutting strategy. Theor. Comput. Sci. 732, 60–72 (2018)
Hay, M., Rastogi, V., Miklau, G., et al.: Boosting the accuracy of differentially private histograms through consistency. Proc. VLDB Endow. 3(1/2), 1021–1032 (2009)
Chen, R., Fung, B.C.M., Desai, B.C.: Differentially private transit data publication: a case study on the Montreal transportation system. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 213–221. ACM, New York (2012)
Shang, T., Zhao, Z., Shu, W., et al.: Algorithm of big data decision tree based on isometric privacy budget allocation. Eng. Sci. Technol. 51(02), 134–140 (2019)
Wang, X., Han, H., Zhang, Z., Yu, Q., Zheng, X.: Budget allocation method for tree index data differential privacy. Comput. Appl. 38(07), 1960–1966 (2008)
Demin, H., Liao, Z.: Differential privacy location privacy protection method for m-fork average tree. J. Small Micro Comput. Syst. 40(03), 76–82 (2019)
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Li, M., Zeng, Y., Guo, Y., Guo, Y. (2020). The Movie Recommendation System Based on Differential Privacy. In: Xiang, Y., Liu, Z., Li, J. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2020. Communications in Computer and Information Science, vol 1298. Springer, Singapore. https://doi.org/10.1007/978-981-15-9031-3_28
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DOI: https://doi.org/10.1007/978-981-15-9031-3_28
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