Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
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Updated
Jul 4, 2024 - Python
Concrete ML: Privacy Preserving ML framework built on top of Concrete, with bindings to traditional ML frameworks.
[CVPR 2023] CaPriDe Learning: Confidential and Private Decentralized Learning based on Encryption-friendly Distillation Loss
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
Intel Paillier Cryptosystem Library is an open-source library which provides accelerated performance of a partial homomorphic encryption (HE), named Paillier cryptosystem, by utilizing Intel® IPP-Crypto technologies on Intel CPUs supporting the AVX512IFMA instructions. The library is written in modern standard C++ and provides the essential API …
A unified framework for privacy-preserving data analysis and machine learning
A Lightweight Partially Homomorphic Encryption Library for Python
Homomorphic Encryption/Decryption with Strings!
The code for a thesis project done spring 2024 at LTH.
Flower framework for Federated Learning, with Fully Homomorphic Encryption integrated
FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.
application that uses homomorphic encryption and Luhn's algorithm to validate credit card numbers without exposing actual card details. This provides a privacy-preserving method to verify sensitive information without compromising security.
Efficient and Straggler-Resistant Homomorphic Encryption for Heterogeneous Federated Learning
Extension of ongoing research project on Arithmetic Circuit Homomorphic encryption (ACHE)
Implementation of the Heflp, a framework enabling practical and overflow-safe federated learning.
Simple emplementation of an election management system, using homomorphic encryption and block chain.
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.
Weavechain Python API
A Fully Homomorphic Encryption (FHE) library for bridging the gap between theory and practice with a focus on performance and accuracy.
PPML with homomorphic encryption
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