PrivSuper

A superset-first approach to frequent Itemset mining under differential privacy

Ning Wang, Xiaokui Xiao, Yin Yang, Zhenjie Zhang, Yu Gu, Ge Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Citations (Scopus)

Abstract

Differential privacy, which has been applied in Google Chrome and Apple iOS, provides strong privacy assurance to users while retaining the capability to discover statistical patterns from sensitive data. We focus on top-k frequent itemset mining on sensitive data, with the goal of obtaining high result utility while satisfying differential privacy. There are two basic methodologies to design a high-utility solution: one uses generic differential privacy mechanisms as building blocks, and minimizes result error through algorithm design. Most existing work follows this approach. The other methodology is to devise a new building block customized for frequent itemset mining. This is much more challenging: To our knowledge, only one recent work, NoisyCut, attempts to do so; unfortunately, Noisycut has been found to violate differential privacy.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages809-820
Number of pages12
ISBN (Electronic)9781509065431
DOIs
Publication statusPublished - 16 May 2017
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: 19 Apr 201722 Apr 2017

Other

Other33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period19/4/1722/4/17

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

Wang, N., Xiao, X., Yang, Y., Zhang, Z., Gu, Y., & Yu, G. (2017). PrivSuper: A superset-first approach to frequent Itemset mining under differential privacy. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (pp. 809-820). [7930027] IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.131

PrivSuper : A superset-first approach to frequent Itemset mining under differential privacy. / Wang, Ning; Xiao, Xiaokui; Yang, Yin; Zhang, Zhenjie; Gu, Yu; Yu, Ge.

Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. p. 809-820 7930027.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, N, Xiao, X, Yang, Y, Zhang, Z, Gu, Y & Yu, G 2017, PrivSuper: A superset-first approach to frequent Itemset mining under differential privacy. in Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017., 7930027, IEEE Computer Society, pp. 809-820, 33rd IEEE International Conference on Data Engineering, ICDE 2017, San Diego, United States, 19/4/17. https://doi.org/10.1109/ICDE.2017.131
Wang N, Xiao X, Yang Y, Zhang Z, Gu Y, Yu G. PrivSuper: A superset-first approach to frequent Itemset mining under differential privacy. In Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society. 2017. p. 809-820. 7930027 https://doi.org/10.1109/ICDE.2017.131
Wang, Ning ; Xiao, Xiaokui ; Yang, Yin ; Zhang, Zhenjie ; Gu, Yu ; Yu, Ge. / PrivSuper : A superset-first approach to frequent Itemset mining under differential privacy. Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017. IEEE Computer Society, 2017. pp. 809-820
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