Log In Sign Up

CrypTFlow2: Practical 2-Party Secure Inference

by   Deevashwer Rathee, et al.

We present CrypTFlow2, a cryptographic framework for secure inference over realistic Deep Neural Networks (DNNs) using secure 2-party computation. CrypTFlow2 protocols are both correct – i.e., their outputs are bitwise equivalent to the cleartext execution – and efficient – they outperform the state-of-the-art protocols in both latency and scale. At the core of CrypTFlow2, we have new 2PC protocols for secure comparison and division, designed carefully to balance round and communication complexity for secure inference tasks. Using CrypTFlow2, we present the first secure inference over ImageNet-scale DNNs like ResNet50 and DenseNet121. These DNNs are at least an order of magnitude larger than those considered in the prior work of 2-party DNN inference. Even on the benchmarks considered by prior work, CrypTFlow2 requires an order of magnitude less communication and 20x-30x less time than the state-of-the-art.


QUOTIENT: Two-Party Secure Neural Network Training and Prediction

Recently, there has been a wealth of effort devoted to the design of sec...

SIRNN: A Math Library for Secure RNN Inference

Complex machine learning (ML) inference algorithms like recurrent neural...

CrypTFlow: Secure TensorFlow Inference

We present CrypTFlow, a first of its kind system that converts TensorFlo...

Secure Medical Image Analysis with CrypTFlow

We present CRYPTFLOW, a system that converts TensorFlow inference code i...

SANNS: Scaling Up Secure Approximate k-Nearest Neighbors Search

We present new secure protocols for approximate k-nearest neighbor searc...

Vectorized Secure Evaluation of Decision Forests

As the demand for machine learning-based inference increases in tandem w...

On the Composability of Statistically Secure Random Oblivious Transfer

We show that stand-alone statistically secure random oblivious transfer ...