Research on Recommender System Model Based on Differential Privacy and Time Series
FAN Li-yun1,2, ZUO Wan-li1,2, WANG Ying1,2, WANG Xin3
1. College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China;
2. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China;
3. School of Computer Technology and Engineering, Changchun Institute of Technology, Changchun, Jilin 130012, China
Abstract:Recommender system is established on users' private information.However,based on results of recommender system,attackers can predict users' states and behaviors.At present,although some researchers focus on collaborative filtering neighbor theory to preserve users' privacy,very few researchers pay enough attention to the model-based privacy-preserving.Differential privacy offers a strong degree of privacy protection by adding noise.And there is interest drift in users' interest.So this paper proposes a recommender system model based on differential privacy theory and time series theory.Firstly,according to differential privacy theory,we add some Laplace-distribution-fitted noises into users' score data to enlarge safety factor in factorization matrix.Then based on Stochastic gradient descent model,we model time series factor as time weight to improve the accuracy of the model.Experimental results demonstrate the accuracy of the algorithm,which provides a valuable perspective for privacy-preserving recommender research.
范利云, 左万利, 王英, 王鑫. 一种基于差分隐私和时序的推荐系统模型研究[J]. 电子学报, 2017, 45(9): 2057-2064.
FAN Li-yun, ZUO Wan-li, WANG Ying, WANG Xin. Research on Recommender System Model Based on Differential Privacy and Time Series. Acta Electronica Sinica, 2017, 45(9): 2057-2064.
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