A unified framework for privacy-preserving data analysis and machine learning
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Updated
Jun 5, 2024 - Python
A unified framework for privacy-preserving data analysis and machine learning
Versatile framework for multi-party computation
This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).
Apache Teaclave (incubating) is an open source universal secure computing platform, making computation on privacy-sensitive data safe and simple.
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.
MPyC: Multiparty Computation in Python
A privacy preserving NLP framework
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
Cloud native Secure Multiparty Computation Stack
Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.
Python library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.
Minimal pure-Python implementation of Shamir's Secret Sharing scheme.
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.
Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.
Secure Aggregation with Shamir’s Method
Oblivious transfer (OT) communications protocol message/response functionality implementations based on Curve25519 and the Ristretto group.
MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.
Fault-tolerant secure multiparty computation in Python.
Collaboration project with Criteo in order to evaluate the relevance of the Secure Multiparty Computation (sMPC) in the context of a Federative Learning
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