Abstract
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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Foundation item: Supported by the National Natural Science Foundation of China (61772562), Major Projects of Technical Innovation of Hubei Province (CXZD2018000035), the Applied Basic Research Project of Wuhan (2017060201010162), the Fundamental Research Funds for the Central Universities (2042017gf0038, YZZ18002), and the Provincial Teaching Research Project of Higher Education in Hubei Province (2017523)
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Cao, M., Wu, F., Ni, M. et al. Differentially Private Top-k Items Based on Least Mean Square——Take E-Commerce Platforms for Example. Wuhan Univ. J. Nat. Sci. 24, 98–106 (2019). https://doi.org/10.1007/s11859-019-1374-x
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DOI: https://doi.org/10.1007/s11859-019-1374-x