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No Peek: A Survey of private distributed deep learning
Praneeth Vepakomma, Tristan Swedish, Ramesh Raskar +2
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still…
A Review of Homomorphic Encryption Libraries for Secure Computation
Sai Sri Sathya, Praneeth Vepakomma, Ramesh Raskar +2
In this paper we provide a survey of various libraries for homomorphic encryption. We describe key features and trade-offs that should be considered while choosing the right approa…
Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish +1
Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method…
Addressing the Invisible: Street Address Generation for Developing Countries with Deep Learning
Ilke Demir, Ramesh Raskar
More than half of the world's roads lack adequate street addressing systems. Lack of addresses is even more visible in daily lives of people in developing countries. We would like…
Flash Photography for Data-Driven Hidden Scene Recovery
Matthew Tancik, Guy Satat, Ramesh Raskar
Vehicles, search and rescue personnel, and endoscopes use flash lights to locate, identify, and view objects in their surroundings. Here we show the first steps of how all these ta…
Distributed learning of deep neural network over multiple agents
Otkrist Gupta, Ramesh Raskar
In domains such as health care and finance, shortage of labeled data and computational resources is a critical issue while developing machine learning algorithms. To address the is…