Search Results for "homomorphic encryption"

This repository gathers Google’s practical tooling for Fully Homomorphic Encryption (FHE), focused on making it possible to run computations on encrypted data without ever decrypting it. At its core is a “transpiler” that converts ordinary functions (typically written in a restricted subset of C++ or similar) into FHE circuits, plus backends that execute those circuits with different FHE libraries.

...Microsoft SEAL is very easy to use, compile and run in many different environments. Homomorphic encryption is an encryption scheme that allows the cloud to compute directly on the encrypted data, without requiring the data to be decrypted first. This results in encrypted computations remaining encrypted, decrypted only by the data owner using the secret key.

FHEVM is a framework built by Zama to enable confidential smart contracts on EVM-compatible blockchains using Fully Homomorphic Encryption (FHE). It lets developers write Solidity contracts that handle encrypted inputs, encrypted state, arithmetic and logical operations on encrypted types, while maintaining privacy (data always encrypted), composability (encrypted state works alongside public state), and security (via threshold decryption, KMS, etc). The framework includes on-chain and off-chain components (contracts, coprocessors, gateway, utilities) to orchestrate the whole encrypted data pipeline.

NVIDIA Federated Learning Application Runtime Environment NVIDIA FLARE is a domain-agnostic, open-source, extensible SDK that allows researchers and data scientists to adapt existing ML/DL workflows(PyTorch, TensorFlow, Scikit-learn, XGBoost etc.) to a federated paradigm. It enables platform developers to build a secure, privacy-preserving offering for a distributed multi-party collaboration. NVIDIA FLARE is built on a componentized architecture that allows you to take federated...

FATE (Federated AI Technology Enabler) is the world's first industrial grade federated learning open source framework to enable enterprises and institutions to collaborate on data while protecting data security and privacy. It implements secure computation protocols based on homomorphic encryption and multi-party computation (MPC). Supporting various federated learning scenarios, FATE now provides a host of federated learning algorithms, including logistic regression, tree-based algorithms, deep learning and transfer learning. FATE became open-source in February 2019. FATE TSC was established to lead FATE open-source community, with members from major domestic cloud computing and financial service enterprises. ...

XFL is a lightweight, high-performance federated learning framework supporting both horizontal and vertical FL. It integrates homomorphic encryption, DP, secure MPC, and optimizes network resilience. Compatible with major ML libraries and deployable via Docker or Conda.

This library implements the fully homomorphic encryption scheme announced by Kumar, Kumar, Pandey and Alam in 2016, in their paper "FHE Scheme with Probabilistic Encryption".