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

作     者：CAO Mengyue WU Fusheng NI Mingtao XIONG Xingxing LIU Shubo WANG Jun 

作者机构：Department of Foreign LanguagesYangtze University College of Arts and Sciences Guizhou Key Laboratory of Economics System SimulationGuizhou University of Finance and Economics School of Cyber Science and EngineeringWuhan University School of Computer Science Leshan Normal University School of ComputerWuhan University College of ComputerSouth-Central University for Nationalities 

出 版 物：《Wuhan University Journal of Natural Sciences》 (武汉大学学报（自然科学英文版）)

年 卷 期：2019年第24卷第2期

页      面：98-106页

核心收录：

学科分类：08[工学] 081201[工学-计算机系统结构] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：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) the Provincial Teaching Research Project of Higher Education in Hubei Province(2017523) 

主　　题：top-k differential privacy least mean square streaming data 

摘      要：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 *** this paper,we explore the problem of differential private top-k items based on least mean ***,we consider the balance between utility and privacy level of released data and improve the precision of top-k based on *** show that our algorithm can achieve differential privacy over streaming data collected and published periodically by server *** 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.