There are 5 repositories under secure-multiparty-computation topic.
A unified framework for privacy-preserving data analysis and machine learning
Apache Teaclave (incubating) is an open source universal secure computing platform, making computation on privacy-sensitive data safe and simple.
This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).
A Privacy-Preserving Framework Based on TensorFlow
SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
Cloud native Secure Multiparty Computation Stack
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.
Minimal pure-Python implementation of a secure multi-party computation (MPC) protocol for evaluating arithmetic sum-of-products expressions via a non-interactive computation phase.
Secure Computation Utilities
Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.
A golang MPC framework that can compile Javascript files into garbled circuits
Fault-tolerant secure multiparty computation in Python.
Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.
MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.
Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)
Collaboration project with Criteo in order to evaluate the relevance of the Secure Multiparty Computation (sMPC) in the context of a Federative Learning
A Python ๐ Secure Multi-Party Computation Sandbox with a Joint Signature Scheme using Elliptic Curve Cryptography โ๏ธ+๐+๐+๐ = ๐
Secure Aggregation with Shamirโs Method
Thesis for Master in Industrial Engineering ICT
Webpage describing the effort and listing contributed documents and artifacts.
Produces a differentially-private model for domain generation algorithm detection.
Private computation of phylogenetic trees based on PHYLIP
Specification of the Mastic Verifiable Distributed Aggregation Function (VDAF)
Data structure for representing secret shares of byte vectors based on bitwise XOR, designed for use within secure multi-party computation (MPC) protocol implementations.
MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.