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
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.
This repository extends the SCALE-MAMBA repository to support external data providers.
Implementation of FedNCF with SecAvg
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”.
This repository contains protocols for SMPC for privacy preserving computation
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
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