54 citations · 93 across the 4 of their papers we have counts for
4 papers
CompactTag: Minimizing Computation Overheads in Actively-Secure MPC for Deep Neural Networks
Yongqin Wang, Pratik Sarkar, Nishat Koti +2
Secure Multiparty Computation (MPC) protocols enable secure evaluation of a circuit by several parties, even in the presence of an adversary who maliciously corrupts all but one of…
MPClan: Protocol Suite for Privacy-Conscious Computations
Nishat Koti, Shravani Patil, Arpita Patra +1
The growing volumes of data being collected and its analysis to provide better services are creating worries about digital privacy. To address privacy concerns and give practical s…
Tetrad: Actively Secure 4PC for Secure Training and Inference
Nishat Koti, Arpita Patra, Rahul Rachuri +1
Mixing arithmetic and boolean circuits to perform privacy-preserving machine learning has become increasingly popular. Towards this, we propose a framework for the case of four par…
SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning
Nishat Koti, Mahak Pancholi, Arpita Patra +1
Performing machine learning (ML) computation on private data while maintaining data privacy, aka Privacy-preserving Machine Learning~(PPML), is an emergent field of research. Recen…