7 citations · 12 across the 3 of their papers we have counts for
3 papers
SCOTCH: An Efficient Secure Computation Framework for Secure Aggregation
Yash More, Prashanthi Ramachandran, Priyam Panda +3
Federated learning enables multiple data owners to jointly train a machine learning model without revealing their private datasets. However, a malicious aggregation server might us…
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine Learning
Arup Mondal, Harpreet Virk, Debayan Gupta
Federated Learning (FL) enables multiple parties to distributively train a ML model without revealing their private datasets. However, it assumes trust in the centralized aggregato…
S++: A Fast and Deployable Secure-Computation Framework for Privacy-Preserving Neural Network Training
Prashanthi Ramachandran, Shivam Agarwal, Arup Mondal +2
We introduce S++, a simple, robust, and deployable framework for training a neural network (NN) using private data from multiple sources, using secret-shared secure function evalua…