A Hybrid Approach to Privacy-Preserving Federated Learning-Reference-Cited by-同舟云学术

A Hybrid Approach to Privacy-Preserving Federated Learning

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 12th ACM Workshop on Artificial Intelligence and Security - AISec'19
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Author:

Truex Stacey¹,Baracaldo Nathalie²,Anwar Ali²,Steinke Thomas²,Ludwig Heiko²,Zhang Rui²,Zhou Yi²

Affiliation:

1. Georgia Institute of Technology, Atlanta, GA, USA

2. IBM Research Almaden, San Jose, CA, USA

Publisher

ACM Press

Reference42 articles.

1. Martin Abadi, Andy Chu, Ian Goodfellow, H Brendan McMahan, Ilya Mironov, Kunal Talwar, and Li Zhang. 2016. Deep learning with differential privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security. ACM, 308--318.

2. Gergely Ács and Claude Castelluccia. 2011. I have a dream!(differentially private smart metering). In International Workshop on Information Hiding. Springer, 118--132.

3. Donald Beaver. 1991. Foundations of secure interactive computing. In Annual International Cryptology Conference. Springer, 377--391.

4. Amos Beimel, Kobbi Nissim, and Eran Omri. 2008. Distributed Private Data Analysis: Simultaneously Solving How and What. In Advances in Cryptology -- CRYPTO 2008, David Wagner (Ed.). Springer Berlin Heidelberg, Berlin, Heidelberg, 451--468.

5. Avrim Blum, Cynthia Dwork, Frank McSherry, and Kobbi Nissim. 2005. Practical privacy: the SuLQ framework. In Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems. ACM, 128--138.

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