SIRNN: A Math Library for Secure RNN Inference

1 code implementation • 10 May 2021 • Deevashwer Rathee, Mayank Rathee, Rahul Kranti Kiran Goli, Divya Gupta, Rahul Sharma, Nishanth Chandran, Aseem Rastogi

Although prior work on secure 2-party inference provides specialized protocols for convolutional neural networks (CNNs), existing secure implementations of these math operators rely on generic 2-party computation (2PC) protocols that suffer from high communication.

Time Series Analysis

Secure Medical Image Analysis with CrypTFlow

1 code implementation • 9 Dec 2020 • Javier Alvarez-Valle, Pratik Bhatu, Nishanth Chandran, Divya Gupta, Aditya Nori, Aseem Rastogi, Mayank Rathee, Rahul Sharma, Shubham Ugare

Our first component is an end-to-end compiler from TensorFlow to a variety of MPC protocols.

Cryptography and Security

CrypTFlow2: Practical 2-Party Secure Inference

1 code implementation • 13 Oct 2020 • Deevashwer Rathee, Mayank Rathee, Nishant Kumar, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation.

CrypTFlow: Secure TensorFlow Inference

4 code implementations • 16 Sep 2019 • Nishant Kumar, Mayank Rathee, Nishanth Chandran, Divya Gupta, Aseem Rastogi, Rahul Sharma

Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security.