From 0c4291bd152286419e7077bf7b2a18ad12034e03 Mon Sep 17 00:00:00 2001 From: Saksham Gupta Date: Wed, 12 Jul 2023 23:37:11 +0530 Subject: [PATCH] Adds EzPC framework (#57) --- readme.md | 1 + 1 file changed, 1 insertion(+) diff --git a/readme.md b/readme.md index 68c5684..f8b42bb 100644 --- a/readme.md +++ b/readme.md @@ -61,6 +61,7 @@ Here I tried to reference the most recent article found on specific software sin - [Carbyne Stack](https://carbynestack.io) - MPC cloud platform that combines state-of-the-art MPC with cloud-native technology like Kubernetes, Istio, and Knative to enable MPC deployments at scale. - [CrypTen](https://github.com/facebookresearch/CrypTen) - MPC with secret sharing; secure against semi-honest adversary; focused on building PyTorch applications. | documentation: [link](https://crypten.ai/) - [EMP-toolkit](https://github.com/emp-toolkit) - 2PC and MPC with garbled circuits; secure against semi-honest adversaries (emp-sh2pc). There are also ones resistant against malicious parties (emp-[ag2pc|m2pc|agmpc]) | eprint: [2017/189](https://eprint.iacr.org/2017/189), [2016/762](https://eprint.iacr.org/2016/762), [2017/030](https://eprint.iacr.org/2017/030). +- [EzPC](https://github.com/mpc-msri/EzPC) - Offers a suite of tools for secure machine learning using semi-honest MPC protocols. It includes a language for secure machine learning, compilers for TensorFlow/Onnx to various MPC protocols, and frameworks for training and inference on deep neural networks. It provides an end-to-end solution for secure machine learning. | documentation: [link](https://github.com/mpc-msri/EzPC). - [Fancy-Garbling](https://github.com/spaceships/fancy-garbling) - 2PC with arithmetic garbled circuits; secure against semi-honest adversaries. | eprint: [2016/969](https://eprint.iacr.org/2016/969). - [FRESCO](http://fresco.readthedocs.io/en/latest/) - MPC supporting TinyTables or SPDZ protocols; secure against semi-honest or malicious adversaries. | [Practice'15](http://practice-project.eu/downloads/publications/D22.1-State-of-the-art-analysis-PU-V1.1.pdf). - [HoneyBadgerMPC](https://github.com/initc3/HoneyBadgerMPC) - Robust MPC-based confidentiality layer for blockchains with guaranteed output delivery; secure against up to t < n/3 malicious parties.