Computer Science ›› 2017, Vol. 44 ›› Issue (5): 81-88.doi: 10.11896/j.issn.1002-137X.2017.05.015

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Research on Application of Differential Privacy in Collaborative Filtering Algorithms

XIAN Zheng-zheng, LI Qi-liang, LI Gai and LI Lei   

  • Online:2018-11-13 Published:2018-11-13

Abstract: Today,the problem of personal privacy inferred by attacker using some background knowledge has become the problems which the Internet users are more worried about.Differential privacy is defined very strictly and can be proved,and it is the most effective privacy protection technology to solve this problem at present.Berlioz et al[1] proposed to apply differential privacy into matrix factorization which is the one of the popular collaborative filtering me-thods.Several new algorisms were proposed by Berlioz et al,but they lacked the strict proof processes.In this paper,we firstly added the prove processes of these algorisms.And then the objective function with added noise method proposed by Chaudhuri was used into the objective function of ALS.In addition,a selection scheme of differential privacy was gi-ven.Finally,some experimental results on two real datasets show that our approach obtains better recommendation accuracy while protecting the personal privacy in the raw data.

Key words: Collaborative filtering,Personal privacy preserving,Differential privacy,Matrix factorization

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