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Differential privacy histogram publishing method based on dynamic sliding window

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Abstract

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 short-term 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.

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Acknowledgements

This work was supported by the National Nature Science Foundation of China (Grant Nos. 91546108, and 71490725), the Anhui Provincial Science and Technology Major Projects (201903a05020020), the Anhui Provincial Natural Science Foundation (1908085QG298), the Fundamental Research Funds for the Central Universities (JZ2019HGTA0053, JZ2019 HGBZ0128), and the Open Research Fund Program of Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, China.

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    Qian Chen, Zhiwei Ni, Xuhui Zhu & Pingfan Xia

  2. Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei, 230009, China

    Qian Chen, Zhiwei Ni, Xuhui Zhu & Pingfan Xia

Authors
  1. Qian Chen
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  2. Zhiwei Ni
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  3. Xuhui Zhu
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  4. Pingfan Xia
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Corresponding author

Correspondence to Zhiwei Ni.

Additional information

Qian Chen received the MS degree in computer science and technology from Anhui University, China in 2019. He is currently a PhD candidate in management science and engineering at Hefei University of Technology, China. His research interests include information security, artificial intelligence, and cloud computing.

Zhiwei Ni received BE and MS degrees in computer software and theory from Anhui University, China. In June 2002, he completed his PhD degree in University of Science and Technology of China, China. Since 2002, he has become a Professor and PhD supervisor in Hefei University of Technology, China, where he has presided over or participated in more than 20 national and provincial projects. From 2010 to 2021, he has served as the director of the Intelligent Management Institute in Hefei University of Technology, China where he has authored three books and more than 100 articles. His research interests include artificial intelligence, machine learning, and cloud computing.

Xuhui Zhu received the BE degree from the School of Mathematics of Hefei University of Technology, China and the PhD degree in management science and engineering from Hefei University of Technology, China. He is currently a Lecturer in Hefei University of Technology, China. His research interests include evolution computation and machine learning.

Pingfan Xia received the MS degree in financial engineering from Anhui University of Finance and Economics, China. She is currently a PhD candidate in management science and engineering at Hefei University of Technology, China. Her research interests include machine learning and internet finance.

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Chen, Q., Ni, Z., Zhu, X. et al. Differential privacy histogram publishing method based on dynamic sliding window. Front. Comput. Sci. 17, 174809 (2023). https://doi.org/10.1007/s11704-022-1651-2

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