Nowadays,user preference data collected from e-commerce platforms have become broadly available,which makes the possibility of data mining and analyzing to improve the users experience of individual purchasing recommendation.While getting the benefits,the publishing data may bring users the risk of privacy disclosure.To solve this problem,differential privacy has been proposed to protect user privacy with theoretical proof,and recently it has been used in streaming data release.However,most of the literature forces on publishing scheme based on Kalman filter,which works well only when knowing the statistical apriority of noise and original data.In this paper,we explore the problem of differentially private top-k items based on least mean square.Specifically,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on post-processing.We show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server provider.We evaluate our algorithm with three real world datasets,and experimental results show that our method enhances the utility with strong privacy protection and outweighs the Kalman filter based existing methods.