Perform data science on data that remains in someone else's server
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Updated
May 16, 2024 - Python
Perform data science on data that remains in someone else's server
A Framework for Encrypted Machine Learning in TensorFlow
Query compiler for secure multi-party computation.
A two-party secure function evaluation using Yao's garbled circuit protocol
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.
Pure-Python implementation of a threshold ecdsa signature scheme based on a secure multi-party computation (MPC) protocol for evaluating arithmetic sum-of-products expressions via a non-interactive computation phase.
Secure aggregation protocol for TensorFlow Federated
A crypto-assisted framework for protecting the privacy of models and queries in inference.
Bridge between TensorFlow and Google's Private Join and Compute library
Flower-based Privacy-Preserving Federated Learning with secure aggregation using Carbyne Stack
A secure computation by secret sharing scheme for multiplication
Fault-tolerant secure multiparty computation in Python.
Python implementation of the TPC protocol from the paper "Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation"
Prepare a Virtual Machine libvirt XML config and the host to match a specific scenario usage
Open-source Python library that allows developers to leverage the nth.community service platform and API to implement secure, privacy-preserving data collaborations within their web services and applications.
This project explores and implements various techniques and protocols using SageMath. It covers topics such as Elliptic Curve Diffie-Hellman (ECDH) key exchange, homomorphic encryption, secure multi-party computation (MPC), queueing theory analysis, and RSA cryptanalysis.
A library for encrypted, privacy preserving machine learning
pySecureCircuit is a Python library that allows secure multiparty computation using Yao's garbled circuit technique. The library provides a way for multiple parties to securely compute a function on their private inputs without revealing them to each other, using a combination of encryption, randomization, and computation over circuits.
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