A unified framework for privacy-preserving data analysis and machine learning
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Updated
Nov 26, 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 v1.2.3 (released on October 30, 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
MPyC: Multiparty Computation in Python
SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.
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Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.
Cloud native Secure Multiparty Computation Stack
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
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.
Python library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.
Curl: Private LLMs through Wavelet-Encoded Look-Up Tables
Minimal pure-Python implementation of Shamir's Secret Sharing scheme.
Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.
Secure Computation Utilities
Fault-tolerant secure multiparty computation in Python.
Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.
Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.
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