Abstract
With the continuous upgrading of smart devices, people are using smartphones more and more frequently. People not only browse the information they need on the Internet, but also more and more people get daily necessities through online shopping. Faced with a variety of recommendation systems, it becomes more and more difficult for people to keep their privacy from being collected while using them. Therefore, ensuring the privacy security of users when they use the recommendation system is increasingly becoming the focus of people. This paper summarizes the related technologies. A recommendation algorithm based on collaborative filtering, matrix factorization as well as the randomized response is proposed, which satisfies local differential privacy (LDP). Besides, this paper also discusses the key technologies used in privacy protection in the recommendation system. Besides, This paper includes the algorithm flow of the recommendation system. Finally, the experiment proves that our algorithm has higher accuracy while guaranteeing user privacy.
The National Natural Science Foundation of China (61572263, 61502251, 61602263, 61872197), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18 0891), the Natural Science Foundation of Jiangsu Province (BK20161516, BK20160916), the Postdoctoral Science Foundation Project of China (2016M601859), the Natural Research Foundation of Nanjing University of Posts and Telecommunications (NY217119).
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References
Shin, H., Kim, S., Shin, J., Xiao, X.: Privacy enhanced matrix factorization for recommendation with local differential privacy. In: IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 9, pp. 1770–1782, 1 September 2018
Hua, J., Xia, C., Zhong, S.: Differentially private matrix factorization. In: IJCAI, pp. 1763–1770 (2015)
Qin, Z., Yang, Y., Yu, T., Khalil, I., Xiao, X., Ren, K.: Heavy hitter estimation over set-valued data with local differential privacy. In: CCS (2016)
Chen, R., Li, H., Qin, A.K., Kasiviswanathan, S.P., Jin, H.: Private spatial data aggregation in the local setting. In: ICDE, pp. 289–300 (2016)
Fanti, G.G., Pihur, V., Erlingsson, U.: Building a RAPPOR with the unknown: privacy-preserving learning of associations and data dictionaries. PoPETS 3, 41–61 (2016)
Nguyen, T., Xiao, X., Yang, Y., Hui, S.C., Shin, H., Shin, J.: Collecting and analyzing data from smart device users with local differential privacy. In: IEEE ICDE arXiv:1907.00782 [cs.DB] (2019)
Shen, Y., Jin, H.: EpicRec: towards practical differentially private framework for personalized recommendation. In: CCS, pp. 180–191 (2016)
Xin, Y., Jaakkola, T.: Controlling privacy in recommender systems. In: NIPS, pp. 2618–2626 (2014)
Bassily, R., Smith, A.D.: Local, private, efficient protocols for succinct histograms. In: STOC, pp. 127–135 (2015)
Fanti, G., et al.: Building a rappor with the unknown: privacy-preserving learning of associations and data dictionaries. PoPETS 2016(3), 41–61 (2016)
Nguyen, T., et al.: Collecting and analyzing data from smart device users with local differential privacy. arXiv preprint arXiv:1606.05053 (2016)
He, X., Machanavajjhala, A., Ding, B.: Blowfish privacy: tuning privacy-utility trade-offs using policies. In: SIGMOD, pp. 1447–1458 (2014)
Zheng, D., Xiong, Y.: A unified probabilistic matrix factorization recommendation algorithm. In: 2018 International Conference on Robots & Intelligent System (ICRIS), Changsha, pp. 246–249 (2018)
Liu, W., Wang, B., Wang, D.: Improved latent factor model in movie recommendation system. In: 2018 International Conference on Intelligent Autonomous Systems (ICoIAS), Singapore, pp. 101–104 (2018)
Sun, H., Dong, B., Wang, H.W., Yu, T., Qin, Z.: Truth inference on sparse crowdsourcing data with local differential privacy. In: 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, pp. 488–497 (2018)
Zhao, X., Li, Y., Yuan, Y., Bi, X., Wang, G.: LDPart: effective location-record data publication via local differential privacy. IEEE Access 7, 31435–31445 (2019)
Lv, L., Zhang, Z., Zhang, L.: A periodic observers synthesis approach for LDP systems based on iteration. IEEE Access 6, 8539–8546 (2018)
Li, N., Qardaji, W., Su, D., Cao, J.: Privbasis: frequent itemset mining with differential privacy. PVLDB 5(11), 1340–1351 (2012)
McSherry, F.D.: Privacy integrated queries: an extensible platform for privacy-preserving data analysis. In: SIGMOD, pp. 19–30 (2009)
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Zhou, H., Yang, G., Xu, Y., Wang, W. (2019). Effective Matrix Factorization for Recommendation with Local Differential Privacy. In: Liu, F., Xu, J., Xu, S., Yung, M. (eds) Science of Cyber Security. SciSec 2019. Lecture Notes in Computer Science(), vol 11933. Springer, Cham. https://doi.org/10.1007/978-3-030-34637-9_18
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DOI: https://doi.org/10.1007/978-3-030-34637-9_18
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