This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).
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Updated
May 24, 2024 - C++
This is the development repository for the OpenFHE library. The current (stable) version is v1.1.4 (released on March 8, 2024).
A unified framework for privacy-preserving data analysis and machine learning
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
Versatile framework for multi-party computation
Cloud native Secure Multiparty Computation Stack
Kuscia(Kubernetes-based Secure Collaborative InfrA) is a K8s-based privacy-preserving computing task orchestration framework.
Fault-tolerant secure multiparty computation in Python.
Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)
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.
Specification of the Mastic Verifiable Distributed Aggregation Function (VDAF)
Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.
Webpage describing the effort and listing contributed documents and artifacts.
Produces a differentially-private model for domain generation algorithm detection.
Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.
Python library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.
Apache Teaclave (incubating) is an open source universal secure computing platform, making computation on privacy-sensitive data safe and simple.
Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.
Secure Computation Utilities
Minimal pure-Python implementation of Shamir's Secret Sharing scheme.
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