Versatile framework for multi-party computation

MPyC: Multiparty Computation in Python

User-friendly secure computation engine based on secure multi-party computation

A maliciously secure two-party computation engine which is embeddable and accessible

An efficient, user-friendly, modular, and extensible framework for mixed-protocol secure multi-party computation with two or more parties

📜 A. Giannopoulos, D. Mouris M.Sc. thesis for University of Athens

open-sourced the SMPCTool.

This the repo for master thesis--SMPC in heavy traffic scenario

基于双云模型的隐私保护神经网络预测框架

The repository is used for presenting the code developed as part of the Adis Hodzic and Casper Knudsens Master Thesis, titled: Stochastic Model Predictive Control of Combined Sewer Overflows in Sanitation Networks

Fault-tolerant secure multiparty computation in Python.

Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.

Implementation of FedNCF with SecAvg

This repository extends the SCALE-MAMBA repository to support external data providers.

Extension of the MOTION2NX framework to implement neural network inferencing task where the data is supplied to the “secure compute servers” by the “data providers”.

An advanced suite of statistical tools harnessing Secure Multi-Party Computation (SMPC) to ensure privacy in survey analysis. Features implementations in Secret Sharing, MPyC, and Jiff. Tailored specifically for the PANAS & BFI-10 questionnaires.

Multiparty computation mockup for research purposes

Privacy-Preserving Location Proximity (based on FHE/MPC)

MP-SPDZ implementation of basic graph theory functionality