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

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

Minimal pure-Python implementation of Shamir's Secret Sharing scheme.

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.

Collaboration project with Criteo in order to evaluate the relevance of the Secure Multiparty Computation (sMPC) in the context of a Federative Learning

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

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

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

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

Secure Aggregation with Shamir’s Method

Specification of the Mastic Verifiable Distributed Aggregation Function (VDAF)

Data structure for representing secret shares of byte vectors based on bitwise XOR, designed for use within secure multi-party computation (MPC) protocol implementations.

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