Towards Faster Functional Encryption for Privacy-preserving Machine Learning-Reference-Cited by-同舟云学术

Towards Faster Functional Encryption for Privacy-preserving Machine Learning

Published:2021-12 Issue: Volume: Page:
ISSN:
Container-title:2021 Third IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)
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Author:

Panzade Prajwal¹,Takabi Daniel¹

Affiliation:

1. Georgia State University,Department of Computer Science,Atlanta,GA,USA

Funder

National Science Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9750214/9750189/09750222.pdf?arnumber=9750222

Reference48 articles.

1. Protocols for secure computations

2. DeepSecure: Scalable Provably-Secure Deep Learning

3. Cryptodl: Deep neural networks over encrypted data;hesamifard;ArXiv Preprint,2017

4. Privacy preserving multi-party machine learning with homomorphic encryption;takabi;The Annual Conference on Neural Information Processing System (NIPS),0

5. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy;gilad-bachrach;International Conference on Machine Learning,0

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