Efficient Privacy Preserving Distributed K-Means for Non-IID Data-Reference-Cited by-同舟云学术

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Efficient Privacy Preserving Distributed K-Means for Non-IID Data

Published:2021 Issue: Volume: Page:439-451
ISSN:0302-9743
Container-title:Advances in Intelligent Data Analysis XIX
language:
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Author:

Brandão André,Mendes Ricardo^ORCID,Vilela João P.^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-74251-5_35

Reference31 articles.

1. Bonawitz, K., et al.: Practical secure aggregation for privacy-preserving machine learning. In: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp. 1175–1191 (2017)

2. Celebi, M.E., Kingravi, H.A., Vela, P.A.: A comparative study of efficient initialization methods for the k-means clustering algorithm. Expert Syst. Appl. 40(1), 200–210 (2013)

3. Clifton, C., Tassa, T.: On syntactic anonymity and differential privacy. In: 2013 IEEE 29th International Conference on Data Engineering Workshops (ICDEW), pp. 88–93. IEEE (2013)

4. Farrand, T., Mireshghallah, F., Singh, S., Trask, A.: Neither private nor fair: impact of data imbalance on utility and fairness in differential privacy. In: Proceedings of the 2020 Workshop on Privacy-Preserving Machine Learning in Practice, pp. 15–19 (2020)

5. Lecture Notes in Computer Science;T Graepel,2013

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