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Discovering top-k patterns with differential privacy-an accurate approach

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Abstract

Frequent pattern mining discovers sets of items that frequently appear together in a transactional database; these can serve valuable economic and research purposes. However, if the database contains sensitive data (e.g., user behavior records, electronic health records), directly releasing the discovered frequent patterns with support counts will carry significant risk to the privacy of individuals. In this paper, we study the problem of how to accurately find the top-k frequent patterns with noisy support counts on transactional databases while satisfying differential privacy. We propose an algorithm, called differentially private frequent pattern (DFP-Growth), that integrates a Laplace mechanism and an exponential mechanism to avoid privacy leakage. We theoretically prove that the proposed method is (λ, δ)-useful and differentially private. To boost the accuracy of the returned noisy support counts, we take consistency constraints into account to conduct constrained inference in the post-processing step. Extensive experiments, using several real datasets, confirm that our algorithm generates highly accurate noisy support counts and top-k frequent patterns.

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Xiaojian Zhang & Xiaofeng Meng

  2. School of Computer and Information Engineering, Henan University of Economics and Law, Zhengzhou, 450002, China

    Xiaojian Zhang

Authors
  1. Xiaojian Zhang
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  2. Xiaofeng Meng
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Corresponding author

Correspondence to Xiaojian Zhang.

Additional information

Xiaojian Zhang received his MS degree in computer science form Yanshan University in 2007. He is now a PhD student at Renmin University of China. His main research interests include differential privacy and data mining.

Xiaofeng Meng received his PhD degree in computer science from the Institute of Computing Technology, Chinese Academy of Sciences. He is a professor and PhD supervisor at Renmin University of China. His research interests include cloud data management, web data management, native XML databases, flash-based databases, and privacy-preserving.

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Zhang, X., Meng, X. Discovering top-k patterns with differential privacy-an accurate approach. Front. Comput. Sci. 8, 816–827 (2014). https://doi.org/10.1007/s11704-014-3230-7

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  • DOI: https://doi.org/10.1007/s11704-014-3230-7

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