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Update readme.md (#64)

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Added Private Benchmarking Platform
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Tanmay Rajore
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@@ -61,7 +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).
- [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). End-to-End Platform: [link](https://github.com/microsoft/private-benchmarking)
- [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.
@@ -113,6 +113,7 @@ Here I tried to reference the most recent article found on specific software sin
- [Garble-Lang](https://github.com/sine-fdn/garble-lang) - Programming language for MPC with Garbled Circuits. Garble is statically typed, low-level, purely functional and uses a syntax heavily inspired by Rust.
- [HyCC](https://gitlab.com/securityengineering/HyCC) - Optimizes circuits for hybrid MPC from ANSI-C. | [CCS'18](https://thomaschneider.de/papers/BDKKS18.pdf).
- [MPC-SoK](https://github.com/MPC-SoK/frameworks) - Build environments for many MPC frameworks using Docker containers. | [S&P19](https://marsella.github.io/static/mpcsok.pdf).
- [Private Benchmarking](https://github.com/microsoft/private-benchmarking) - Provides an End to End platform to perform Private Benchmarking using MPC and GPU-based Confidential Computing on LLMs/CNNs/Vision Models using [EzPC](https://github.com/mpc-msri/EzPC) framework. | [eprint](https://arxiv.org/abs/2403.00393).
- [Tiny-Garble](https://github.com/esonghori/TinyGarble) - Logic Synthesis and Sequential Descriptions for Yao's Garbled Circuits. | [S&P'15](http://aceslab.org/sites/default/files/TinyGarble.pdf).
- [UC Compiler](https://github.com/encryptogroup/UC) - Valiant's Universal Circuit Compiler. | [2016/093](https://eprint.iacr.org/2016/093).