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

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Publisher: Springer
Document Type: Report
Length: 324 words

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Byline: Mengyue Cao (1), Fusheng Wu (2), Mingtao Ni (3,4), Xingxing Xiong (5), Shubo Liu (5), Jun Wang (6) Keywords: top-k; differential privacy; least mean square; streaming data; TP 305 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. Author Affiliation: (1) grid.410654.2, Department of Foreign Languages, Yangtze University College of Arts and Sciences, Jingzhou, Hubei, 434020, China (2) grid.443393.a, Guizhou Key Laboratory of Economics System Simulation, Guizhou University of Finance and Economics, Guiyang, Guizhou, 550025, China (3) 0000 0001 2331 6153, grid.49470.3e, School of Cyber Science and Engineering, Wuhan University, Wuhan, Hubei, 430074, China (4) 0000 0000 9195 8580, grid.459727.a, School of Computer Science, Leshan Normal University, Leshan, Sichuan, 614000, China (5) 0000 0001 2331 6153, grid.49470.3e, School of Computer, Wuhan University, Wuhan, Hubei, 430074, China (6) 0000 0000 9147 9053, grid.412692.a, College of Computer, South-Central University for Nationalities, Wuhan, Hubei, 430074, China Article History: Registration Date: 19/03/2019 Received Date: 23/06/2018 Online Date: 20/03/2019 Article note: 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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Gale Document Number: GALE|A579551800