Private Benchmarking to Prevent Contamination and Improve Comparative Evaluation of LLMs

no code implementations • 1 Mar 2024 • Nishanth Chandran, Sunayana Sitaram, Divya Gupta, Rahul Sharma, Kashish Mittal, Manohar Swaminathan

To solve this problem, we propose Private Benchmarking, a solution where test datasets are kept private and models are evaluated without revealing the test data to the model.

Benchmarking

Privacy Preserving Multi-Agent Reinforcement Learning in Supply Chains

no code implementations • 9 Dec 2023 • Ananta Mukherjee, Peeyush Kumar, Boling Yang, Nishanth Chandran, Divya Gupta

To tackle this challenge, we propose a game-theoretic, privacy-preserving mechanism, utilizing a secure multi-party computation (MPC) framework in MARL settings.

Multi-agent Reinforcement Learning Policy Gradient Methods +2

Efficient ML Models for Practical Secure Inference

no code implementations • 26 Aug 2022 • Vinod Ganesan, Anwesh Bhattacharya, Pratyush Kumar, Divya Gupta, Rahul Sharma, Nishanth Chandran

For instance, the model provider could be a diagnostics company that has trained a state-of-the-art DenseNet-121 model for interpreting a chest X-ray and the user could be a patient at a hospital.

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.

EinsteinPy: A Community Python Package for General Relativity

1 code implementation • 22 May 2020 • Shreyas Bapat, Ritwik Saha, Bhavya Bhatt, Shilpi Jain, Akshita Jain, Sofía Ortín Vela, Priyanshu Khandelwal, Jyotirmaya Shivottam, Jialin Ma, Gim Seng Ng, Pratyush Kerhalkar, Hrishikesh Sudam Sarode, Rishi Sharma, Manvi Gupta, Divya Gupta, Tushar Tyagi, Tanmay Rustagi, Varun Singh, Saurabh Bansal, Naman Tayal, Abhijeet Manhas, Raphael Reyna, Gaurav Kumar, Govind Dixit, Ratin Kumar, Sashank Mishra, Alpesh Jamgade, Raahul Singh, Rohit Sanjay, Khalid Shaikh, Bhavam Vidyarthi, Shamanth R Nayak K, Vineet Gandham, Nimesh Vashistha, Arnav Das, Saurabh, Shreyas Kalvankar, Ganesh Tarone, Atul Mangat, Suyog Garg, Bibek Gautam, Sitara Srinivasan, Aayush Gautam, Swaastick Kumar Singh, Suyash Salampuria, Zac Yauney, Nihar Gupte, Gagan Shenoy, Micky Yun Chan

Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.

General Relativity and Quantum Cosmology Cosmology and Nongalactic Astrophysics Instrumentation and Methods for Astrophysics 83-04

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.