Differentially Private Frequent Itemset Mining Against Incremental Updates-Reference-Cited by-同舟云学术

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Differentially Private Frequent Itemset Mining Against Incremental Updates

Published:2020 Issue: Volume: Page:649-667
ISSN:0302-9743
Container-title:Information and Communications Security
language:
Short-container-title:

Author:

Liang Wenjuan,Chen Hong,Wu Yuncheng,Li Cuiping

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-41579-2_38

Reference28 articles.

1. Calandrino, J.A., et al.: You might also like: privacy risks of collaborative filtering. In: IEEE Symposium on Security and Privacy, pp. 231–246 (2011)

2. Soria-Comas, J., Domingo-Ferrer, J.: Big data privacy: challenges to privacy principles and models. Data Sci. Eng. 1(1), 25–36 (2016)

3. Lecture Notes in Computer Science;C Dwork,2006

4. McSherry, F., Talwar, K.: Mechanism design via differential privacy. In: FOCS, pp. 94–103 (2007)

5. Su, S., Xu, S., et al.: Differentially private frequent itemset mining via transaction splitting. In: ICDE, pp. 1564–1565 (2016)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Differential Privacy Frequent Closed Itemset Mining over Data Stream;2023 IEEE 22nd International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom);2023-11-01

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