【摘 要】
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User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause se-rious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we propose a differ-entially private user
【机 构】
：
Information Technology School,Hebei University of Economics and Business,Shijiazhuang 050061,China;T
论文部分内容阅读
User profiles are widely used in the age of big data.However,generating and releasing user profiles may cause se-rious privacy leakage,since a large number of personal data are collected and analyzed.In this paper,we propose a differ-entially private user profile construction method DP-UserPro,which is composed of DP-CLIQUE and privately top-k tags selection.DP-CLIQUE is a differentially private high dimen-sional data cluster algorithm based on CLIQUE.The multi-dimensional 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 ceils are clustered into a cluster.Then a privately top-k tags selection approach is pro-posed based on the score function of each tag,to select the most important k tags which can represent the characteristics of the cluster.Privacy and utility of DP-UserPro are theoretically ana-lyzed and experimentally evaluated in the last.Comparison ex-periments 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.
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