Versatile framework for multi-party computation

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.

A privacy preserving NLP framework

Synergistic fusion of privacy-enhancing technologies for enhanced privacy protection.

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.

Updatable Private Set Intersection Revisited: Extended Functionalities, Deletion, and Worst-Case Complexity (Asiacrypt 2024)

TypeScript library for working with encrypted data within nilDB queries and replies.

Curl: Private LLMs through Wavelet-Encoded Look-Up Tables

Minimal pure-Python implementation of Shamir's secret sharing scheme.

SecretFlow-Serving is a serving system for privacy-preserving machine learning models.

Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.

Python library for working with encrypted data within nilDB queries and replies.

Your GoTo Library for NN's over MPC

Fault-tolerant secure multiparty computation in Python.

Centralized asynchronous secure aggregation using Shamir's secret sharing for the Boston Women's Workforce Council.

Extremely Randomized Trees with Privacy Preservation for Distributed Data (k-PPD-ERT)

Oblivious transfer (OT) communications protocol message/response functionality implementations based on Curve25519 and the Ristretto group.