Abstract
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-k tags 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-k tags selection approach is proposed 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 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.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 62002098), Natural Science Foundation of Hebei Province (F2020207001, F2019207061), the Scientific Research Projects of Hebei Education Department (QN2018116), and the Research Foundation of Hebei University of Economics and Business (2018QZ04, 2019JYQ08).
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Zheng Huo received her BS and MS degree in Computer Science and Technology from Yanshan University, China in 2004 and 2007, and the PhD degree in Computer Software and Theory from Renmin University of China, China in 2013. She is now an associate professor in Hebei University of Economics and Business, China. Her research interests include mobile data management and privacy-preserving techniques.
Ping He received the PhD degree in computer science and technology from Beijing Jiaotong University, China in 2015. She is now an associate professor in Hebei University of Economics and Business, China. Her research interests include design and analysis of algorithms for optimization problems in wireless networks and data dissemination, high performance networks.
Lisha Hu received her BS degree in Information and Computation Science and MS degree in Applied Mathematics from Hebei University, China in 2009 and 2012, respectively. She received her the PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences, China in 2017. She is currently a lecturer in Hebei University of Economics and Business, China. Her research interests include machine learning and activity recognition.
Huanyu Zhao received the MS degree in applied mathematics from Hebei University, China in 2009. He is currently an associate professor in Institute of Applied Mathematics, Hebei Academy of Sciences, and School of Computer and Data Engineering, Zhejiang University, China. His current research interests include stream data compression and processing, machine learning, evolutionary computation, and computing algorithm.
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Huo, Z., He, P., Hu, L. et al. DP-UserPro: differentially private user profile construction and publication. Front. Comput. Sci. 15, 155811 (2021). https://doi.org/10.1007/s11704-020-9462-9
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DOI: https://doi.org/10.1007/s11704-020-9462-9