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Mean estimation over numeric data with personalized local differential privacy

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Abstract

The fast development of the Internet and mobile devices results in a crowdsensing business model, where individuals (users) are willing to contribute their data to help the institution (data collector) analyze and release useful information. However, the reveal of personal data will bring huge privacy threats to users, which will impede the wide application of the crowdsensing model. To settle the problem, the definition of local differential privacy (LDP) is proposed. Afterwards, to respond to the varied privacy preference of users, researchers propose a new model, i.e., personalized local differential privacy (PLDP), which allow users to specify their own privacy parameters. In this paper, we focus on a basic task of calculating the mean value over a single numeric attribute with PLDP. Based on the previous schemes for mean estimation under LDP, we employ PLDP model to design novel schemes (LAP, DCP, PWP) to provide personalized privacy for each user. We then theoretically analysis the worst-case variance of three proposed schemes and conduct experiments on synthetic and real datasets to evaluate the performance of three methods. The theoretical and experimental results show the optimality of PWP in the low privacy regime and a slight advantage of DCP in the high privacy regime.

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Acknowledgements

This work was partly supported by the National Key Research and Development Program of China (2020YFB1005900), the Research Fund of Guangxi Key Laboratory of Trusted Software (kx202034), the Team Project of Collaborative Innovation in Universities of Gansu Province (2017C-16) and Collaborative Innovation Center of Novel Software Technology and Industrialization.

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Authors and Affiliations

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China

    Qiao Xue, Youwen Zhu & Jian Wang

  2. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Youwen Zhu

  3. School of Cyber Security, Gansu University of Political Science and Law, Lanzhou, 730070, China

    Youwen Zhu

Authors
  1. Qiao Xue
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  2. Youwen Zhu
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  3. Jian Wang
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Corresponding author

Correspondence to Youwen Zhu.

Additional information

Qiao Xue received her BE degree and PhD degree in Nanjing University of Aeronautics and Astronautics, China in 2015 and 2020, respectively. She is currently a postdoctoral fellow in Hong Kong Polytechnic University, China. Her research interests include information security and data privacy.

Youwen Zhu received his BE degree and PhD degree in Computer Science from University of Science and Technology of China, China in 2007 and 2012, respectively. From 2012 to 2014, he is a JSPS postdoctor in Kyushu University, Japan. He is currently a Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. He has published more than 40 papers in refereed international conferences and journals, and has served as program committee member in several international conferences. His research interests include identity authentication, information security and data privacy.

Jian Wang received the PhD degree in Nanjing University, China in 1998. He is currently a Professor at the College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, China. His research interests include cryptographic protocol and malicious tracking.

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Xue, Q., Zhu, Y. & Wang, J. Mean estimation over numeric data with personalized local differential privacy. Front. Comput. Sci. 16, 163806 (2022). https://doi.org/10.1007/s11704-020-0103-0

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