A unified framework for privacy-preserving data analysis and machine learning
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Updated
Jun 17, 2024 - Python
A unified framework for privacy-preserving data analysis and machine learning
MPyC: Multiparty Computation in Python
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)
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.
Minimal pure-Python implementation of Shamir's Secret Sharing scheme.
Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.
Data structure for representing secret shares of byte vectors based on bitwise XOR, designed for use within secure multi-party computation (MPC) protocol implementations.
A Python 🐍 Secure Multi-Party Computation Sandbox with a Joint Signature Scheme using Elliptic Curve Cryptography ✉️+🔑+🔑+🔑 = 🔓
A privacy preserving NLP framework
MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.
MPC management framework automating a secure network setup among participants of multiparty computation in the outsourced setting.
Oblivious transfer (OT) communications protocol message/response functionality implementations based on Curve25519 and the Ristretto group.
Collaboration project with Criteo in order to evaluate the relevance of the Secure Multiparty Computation (sMPC) in the context of a Federative Learning
Secure Aggregation with Shamir’s Method
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