Paper 2019/1365
FLASH: Fast and Robust Framework for Privacy-preserving Machine Learning
Megha Byali, Harsh Chaudhari, Arpita Patra, and Ajith Suresh
Abstract
Privacy-preserving machine learning (PPML) via Secure Multi-party Computation (MPC) has gained momentum in the recent past. Assuming a minimal network of pair-wise private channels, we propose an efficient four-party PPML framework over rings
Metadata
- Available format(s)
-
PDF
- Category
- Cryptographic protocols
- Publication info
- Published elsewhere. Major revision. Privacy Enhancing Technologies Symposium (PETS) 2020
- Keywords
- PrivacyMachine LearningRobust 4PC
- Contact author(s)
-
chaudharim @ iisc ac in
ajith @ iisc ac in
arpita @ iisc ac in
megha @ iisc ac in - History
- 2020-02-20: last of 2 revisions
- 2019-11-27: received
- See all versions
- Short URL
- https://ia.cr/2019/1365
- License
-
CC BY
BibTeX
@misc{cryptoeprint:2019/1365, author = {Megha Byali and Harsh Chaudhari and Arpita Patra and Ajith Suresh}, title = {{FLASH}: Fast and Robust Framework for Privacy-preserving Machine Learning}, howpublished = {Cryptology {ePrint} Archive, Paper 2019/1365}, year = {2019}, url = {https://eprint.iacr.org/2019/1365} }