354 citations · 956 across the 24 of their papers we have counts for
10 papers · 1 filter
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks
Abhishek Singh, Ayush Chopra, Vivek Sharma +4
Recent deep learning models have shown remarkable performance in image classification. While these deep learning systems are getting closer to practical deployment, the common assu…
NoPeek: Information leakage reduction to share activations in distributed deep learning
Praneeth Vepakomma, Abhishek Singh, Otkrist Gupta +1
For distributed machine learning with sensitive data, we demonstrate how minimizing distance correlation between raw data and intermediary representations reduces leakage of sensit…
PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for COVID-19 Pandemic
Priyanka Singh, Abhishek Singh, Gabriel Cojocaru +2
Several contact tracing solutions have been proposed and implemented all around the globe to combat the spread of COVID-19 pandemic. But, most of these solutions endanger the priva…
SplitNN-driven Vertical Partitioning
Iker Ceballos, Vivek Sharma, Eduardo Mugica +4
In this work, we introduce SplitNN-driven Vertical Partitioning, a configuration of a distributed deep learning method called SplitNN to facilitate learning from vertically distrib…
FedML: A Research Library and Benchmark for Federated Machine Learning
Chaoyang He, Songze Li, Jinhyun So +17
Federated learning (FL) is a rapidly growing research field in machine learning. However, existing FL libraries cannot adequately support diverse algorithmic development; inconsist…
Splintering with distributions: A stochastic decoy scheme for private computation
Praneeth Vepakomma, Julia Balla, Ramesh Raskar
Performing computations while maintaining privacy is an important problem in todays distributed machine learning solutions. Consider the following two set ups between a client and…