82 citations · 150 across the 10 of their papers we have counts for
12 papers
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…
COVID-19 Contact-Tracing Mobile Apps: Evaluation and Assessment for Decision Makers
Ramesh Raskar, Greg Nadeau, John Werner +21
A number of groups, from governments to non-profits, have quickly acted to innovate the contact-tracing process: they are designing, building, and launching contact-tracing apps in…
Assessing Disease Exposure Risk with Location Data: A Proposal for Cryptographic Preservation of Privacy
Alex Berke, Michiel Bakker, Praneeth Vepakomma +2
Governments and researchers around the world are implementing digital contact tracing solutions to stem the spread of infectious disease, namely COVID-19. Many of these solutions t…
Split Learning for collaborative deep learning in healthcare
Maarten G. Poirot, Praneeth Vepakomma, Ken Chang +3
Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-cen…