User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause serious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we p...User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause serious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we propose a differentially private user profile construction method DP-UserPro,which is composed of DP-CLIQUE and privately top-κtags selection.DP-CLIQUE is a differentially private high dimensional data cluster algorithm based on CLIQUE.The multidimensional tag space is divided into cells,Laplace noises are added into the count value of each cell.Based on the breadth-first-search,the largest connected dense cells are clustered into a cluster.Then a privately top-κtags selection approach is proposed based on the score function of each tag,to select the most importantκtags which can represent the characteristics of the cluster.Privacy and utility of DP-UserPro are theoretically analyzed and experimentally evaluated in the last.Comparison experiments are carried out with Tag Suppression algorithm on two real datasets,to measure the False Negative Rate(FNR)and precision.The results show that DP-UserPro outperforms Tag Suppression by 62.5%in the best case and 14.25%in the worst case on FNR,and DP-UserPro is about 21.1%better on precision than that of Tag Suppression,in average.展开更多
基金the National Natural Science Foundation of China(Grant No.62002098)Natural Science Foundation of Hebei Province(F2020207001,F2019207061)+1 种基金the Scientific Research Projects of Hebei Education Department(QN2018116)the Research Foundation of Hebei University of Economics and Business(2018QZ04,2019JYQ08).
文摘User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause serious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we propose a differentially private user profile construction method DP-UserPro,which is composed of DP-CLIQUE and privately top-κtags selection.DP-CLIQUE is a differentially private high dimensional data cluster algorithm based on CLIQUE.The multidimensional tag space is divided into cells,Laplace noises are added into the count value of each cell.Based on the breadth-first-search,the largest connected dense cells are clustered into a cluster.Then a privately top-κtags selection approach is proposed based on the score function of each tag,to select the most importantκtags which can represent the characteristics of the cluster.Privacy and utility of DP-UserPro are theoretically analyzed and experimentally evaluated in the last.Comparison experiments are carried out with Tag Suppression algorithm on two real datasets,to measure the False Negative Rate(FNR)and precision.The results show that DP-UserPro outperforms Tag Suppression by 62.5%in the best case and 14.25%in the worst case on FNR,and DP-UserPro is about 21.1%better on precision than that of Tag Suppression,in average.
