Versatile framework for multi-party computation
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Updated
Jun 4, 2024 - C++
Versatile framework for multi-party computation
A library for lattice-based multiparty homomorphic encryption in Go
MPyC: Multiparty Computation in Python
Threshold Signature Scheme for ECDSA
Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)
A maliciously secure two-party computation engine which is embeddable and accessible
Secure Linear Regression in the Semi-Honest Two-Party Setting.
Verifiable/deterministic fair tickets generation for lotteries, raffles and gambling games. 🦏 🍀
Extension of the MOTION2NX framework to implement neural network inferencing task where the data is supplied to the “secure compute servers” by the “data providers”.
MPC implementation of proof of custody
HellHound is a decentralized blind computation platform.
Credit Approval Classification Deep Learning Model using Differential Drivacy, Secure Multi-Party Computation, and Federated Learning
Python library that serves as an API for common cryptographic primitives used to implement OPRF, OT, and PSI protocols.
Material supporting the tutorial "Pursuing Privacy in Recommender Systems: The View of Users and Researchers from Regulations to Applications" held at the 15th ACM Conference on Recommender Systems in Amsterdam, Netherlands
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.
Data structure for representing additive secret shares of integers, designed for use within secure multi-party computation (MPC) protocol implementations.
A scheme that produces a zero-knowledge proof of correctness for an MPC computation. The scheme allows anyone, particularly someone external to the secure computation, to check the correctness of the output, while preserving the privacy properties of the MPC protocol.
Delta Development Documentation
Paper list and relevant material for Privacy-Preserving Computation.
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