MPC-Minimized Secure LLM Inference

no code implementations • 7 Aug 2024 • Deevashwer Rathee, Dacheng Li, Ion Stoica, Hao Zhang, Raluca Popa

Many inference services based on large language models (LLMs) pose a privacy concern, either revealing user prompts to the service or the proprietary weights to the user.

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

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.