Versatile framework for multi-party computation
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Updated
Jun 18, 2024 - C++
Versatile framework for multi-party computation
MPyC: Multiparty Computation in Python
An efficient, user-friendly, modular, and extensible framework for mixed-protocol secure multi-party computation with two or more parties
A maliciously secure two-party computation engine which is embeddable and accessible
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”.
User-friendly secure computation engine based on secure multi-party computation
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
open-sourced the SMPCTool.
📜 A. Giannopoulos, D. Mouris M.Sc. thesis for University of Athens
Fault-tolerant secure multiparty computation in Python.
This the repo for master thesis--SMPC in heavy traffic scenario
Multiparty computation mockup for research purposes
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.
Webpage describing the effort and listing contributed documents and artifacts.
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.
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