28 results for “topic:smpc”
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
No description provided.
This the repo for master thesis--SMPC in heavy traffic scenario
open-sourced the SMPCTool.
📜 A. Giannopoulos, D. Mouris M.Sc. thesis for University of Athens
MPC Protocols for the Stoffel framework
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.
基于双云模型的隐私保护神经网络预测框架
Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.
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”.
Implementation of FedNCF with SecAvg
Secure Multiparty Computation Protocols written in C++ for efficiency and scalability
This repository contains protocols for SMPC for privacy preserving computation
An example of a custom implementation of Secure Mulriparty Computation (SMPC) protocol using additive secret sharing for secure federated learning over the Flower library.
No description provided.
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.
Secure Multi-Party Computation (SMPC), 安全多方验证计算——百万富翁问题
Privacy-Preserving Location Proximity (based on FHE/MPC)
Multiparty computation mockup for research purposes
Webpage describing the effort and listing contributed documents and artifacts.
This is about implementing decentralising and other security measures while training ML models for security
blockchain based federated learning system using secure multiparty computation for healthcare data privacy and model verification
No description provided.