Differentially Private Top-k Items Based on Least Mean Square——Take E-Commerce Platforms for Example

User preference data broadly collected from e-commerce platforms have benefits to improve the user’s experience of individual purchasing recommendation by data mining and analyzing, which may bring users the risk of privacy disclosure. In this paper, we explore the problem of differential 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 datasets, and the experimental results show that the precision of our method reaches 85% with strong privacy protection, which outperforms the Kalman filter-based existing methods.

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