A unified framework for privacy-preserving data analysis and machine learning

MPyC: Multiparty Computation in Python

A privacy preserving NLP 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.

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

Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.

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.

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

A Python 🐍 Secure Multi-Party Computation Sandbox with a Joint Signature Scheme using Elliptic Curve Cryptography ✉️+🔑+🔑+🔑 = 🔓

Secure Aggregation with Shamir’s Method

MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.

Specification of the Mastic Verifiable Distributed Aggregation Function (VDAF)

MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.

A lightweight, pip-installable version of FedscGen.