7 citations · 13 across the 6 of their papers we have counts for
6 papers
Tight Short-Lived Signatures
Arup Mondal, Ruthu Hulikal Rooparaghunath, Debayan Gupta
A Time-lock puzzle (TLP) sends information into the future: a predetermined number of sequential computations must occur (i.e., a predetermined amount of time must pass) to retriev…
RANDGENER: Distributed Randomness Beacon from Verifiable Delay Function
Arup Mondal, Ruthu Hulikal Rooparaghunath, Debayan Gupta
Buoyed by the excitement around secure decentralized applications, the last few decades have seen numerous constructions of distributed randomness beacons (DRB) along with use case…
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…
Flatee: Federated Learning Across Trusted Execution Environments
Arup Mondal, Yash More, Ruthu Hulikal Rooparaghunath +1
Federated learning allows us to distributively train a machine learning model where multiple parties share local model parameters without sharing private data. However, parameter e…
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…