Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the prem...Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and analysis.The dynamic data release is a study with a wide range of current industry needs.However,the amount of data varies considerably over different periods.Unreasonable data processing will result in the risk of users’information leakage and unavailability of the data.Therefore,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data privacy.DPHP-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several windows.DPHK+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic data.Extensive experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.展开更多
基金supported by the National Nature Science Foundation of China(Grant Nos.91546108,and 71490725)the AnhuiProvincial Scienceand Technology Major Projects(201903a05020020)+2 种基金the Anhui Provincial Natural Science Foundation(1908085QG298)the Fundamental Research Funds for the Central Universities(JZ2019HGTA0053,JZ2019 HGBZ0128)the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making,Ministry of Education,China.
文摘Differential privacy has recently become a widely recognized strict privacy protection model of data release.Differential privacy histogram publishing can directly show the statistical data distribution under the premise of ensuring user privacy for data query,sharing,and analysis.The dynamic data release is a study with a wide range of current industry needs.However,the amount of data varies considerably over different periods.Unreasonable data processing will result in the risk of users’information leakage and unavailability of the data.Therefore,we designed a differential privacy histogram publishing method based on the dynamic sliding window of LSTM(DPHP-DL),which can improve data availability on the premise of guaranteeing data privacy.DPHP-DL is integrated by DSW-LSTM and DPHK+.DSW-LSTM updates the size of sliding windows based on data value prediction via long shortterm memory(LSTM)networks,which evenly divides the data stream into several windows.DPHK+heuristically publishes non-isometric histograms based on k-mean++clustering of automatically obtaining the optimal K,so as to achieve differential privacy histogram publishing of dynamic data.Extensive experiments on real-world dynamic datasets demonstrate the superior performance of the DPHP-DL.
