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An effective scheme for top-k frequent itemset mining under differential privacy conditions

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61- 532021, 61772537, 61772536, 61702522) and National Key R&D Program of China (Grant No. 2018YFB1004400).

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Authors and Affiliations

  1. Key Lab of Data Engineering and Knowledge Engineering of MOE, Renmin University of China, Beijing, 100872, China

    Wenjuan Liang, Hong Chen, Jing Zhang, Dan Zhao & Cuiping Li

  2. College of Computer and Information Engineering, Henan University, Kaifeng, 475001, China

    Wenjuan Liang

Authors
  1. Wenjuan Liang
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  2. Hong Chen
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  3. Jing Zhang
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  4. Dan Zhao
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  5. Cuiping Li
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Corresponding author

Correspondence to Jing Zhang.

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Liang, W., Chen, H., Zhang, J. et al. An effective scheme for top-k frequent itemset mining under differential privacy conditions. Sci. China Inf. Sci. 63, 159101 (2020). https://doi.org/10.1007/s11432-018-9849-y

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  • DOI: https://doi.org/10.1007/s11432-018-9849-y

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