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Why current differential privacy schemes are inapplicable for correlated data publishing?

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Abstract

Although data analysis and mining technologies can efficiently provide intelligent and personalized services to us, data owners may not always be willing to share their true data because of privacy concerns. Recently, differential privacy (DP) technology has achieved a good trade-off between data utility and privacy guarantee by publishing noisy outputs. Nonetheless, DP still has a risk of privacy leakage when handling correlated data directly. Current schemes attempt to extend DP to publish correlated data, but are faced with the challenge of violating DP or low-level data utility. In this paper, we try to explore the essential cause of this inapplicability. Specifically, we suppose that this inapplicability is caused by the different correlations between noise and original data. To verify our supposition, we propose the notion of Correlation-Distinguishability Attack (CDA) to separate IID (Independent and Identically Distributed) noise from correlated data. Furthermore, taking time series as an example, we design an optimum filter to realize CDA in practical applications. Experimental results support our supposition and show that, the privacy degree of current approaches has a degradation under CDA.

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  1. http://www.cdc.gov/flu/

  2. http://research.stlouisfed.org/

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Acknowledgments

This work was supported in part by the Science and Technology Research Project of Chongqing Education Commission (KJQN201900612) and the PhD Starts Fund Project of Chongqing University of Posts and Telecommunications (A2020-46). The authors are grateful for the anonymous reviewers who made constructive comments and improvements.

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Authors and Affiliations

  1. Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology, College of Computer Science and Technology,, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China

    Hao Wang, Ying Xia & Xu Zhang

  2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Zhengquan Xu & Shan Jia

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  1. Hao Wang
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  2. Zhengquan Xu
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  3. Shan Jia
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  4. Ying Xia
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  5. Xu Zhang
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Correspondence to Zhengquan Xu.

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Wang, H., Xu, Z., Jia, S. et al. Why current differential privacy schemes are inapplicable for correlated data publishing?. World Wide Web 24, 1–23 (2021). https://doi.org/10.1007/s11280-020-00825-8

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