Paper 2023/1890
Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries
Abstract
Privacy-preserving machine learning (PPML) techniques have gained significant popularity in the past years. Those protocols have been widely adopted in many real-world security-sensitive machine learning scenarios, e.g., medical care and finance. In this work, we introduce
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Preprint.
- Contact author(s)
- lutianpei @ zju edu cn
- History
- 2024-05-29: last of 2 revisions
- 2023-12-08: received
- See all versions
- Short URL
- https://ia.cr/2023/1890
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2023/1890, author = {Tianpei Lu and Bingsheng Zhang and Lichun Li and Kui Ren}, title = {Aegis: A Lightning Fast Privacy-preserving Machine Learning Platform against Malicious Adversaries}, howpublished = {Cryptology {ePrint} Archive, Paper 2023/1890}, year = {2023}, url = {https://eprint.iacr.org/2023/1890} }