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.