电子学报2017，Vol.45Issue(9)：2057-2064,8.DOI:10.3969/j.issn.0372-2112.2017.09.001
一种基于差分隐私和时序的推荐系统模型研究
Research on Recommender System Model Based on Differential Privacy and Time Series
摘要
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.关键词
推荐系统/非负矩阵分解/随机梯度下降法/差分隐私/时序理论Key words
recommender system/non-negative matrix factorization/Stochastic gradient descent/differential privacy/time series分类
信息技术与安全科学引用本文复制引用
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):2057-2064,8.基金项目
国家自然科学基金(No.60973040,No.61300148,No.61602057) （No.60973040,No.61300148,No.61602057）
吉林省科技发展计划(No.20130206051GX,No.20130522112JH,No.20170520059JH) （No.20130206051GX,No.20130522112JH,No.20170520059JH）
