Sight AI
  • 👋Overview
    • đŸĨFHE
    • 🍍The Problem
  • 🍇Sight Oracle
    • 💡Concepts
      • 🍉Architecture
      • 📔Contract
      • 🟠Decentralized Oracle Node Network
      • 📠FHE Compute Execution Virtual Machine
      • 📃RequestBuilder
    • ⏊Supported Networks
  • Sight Contracts
    • Getting Started
    • Understanding Request Model
  • SIGHT.JS
    • Getting Started
    • Make an encrypt value - CLI
    • Make an encrypt value - Frontend
  • â„šī¸Tutorial
    • Environment Setup
      • Prerequisite
      • Setup Local Development Environment
      • Fund Local Wallet
    • 👷Create Your First dAPP using Hardhat
    • đŸ› ī¸Create Your First dAPP using Foundry
    • 🎲Create a Crypto Pusher Game
      • Contract
      • Interface
    • 📄Remix FHE Demo Local Deployment Tutorial- Preparation
    • 📄Remix FHE Demo Local Deployment Tutorial- Deploying Contracts Using Remix
    • 📄Remix FHE Demo Local Deployment Tutorial- Contracts Explanation&Interaction
  • 🔎Research
    • ✅Verifiable FHE
    • 🌟SPARC
    • 📅Research Updates
  • 🍓Innovative Use Cases and dApps
    • đŸĨĨFHE On-Chain Game
    • A Fairer Prediction Markets with Sight Oracle
  • đŸģRoadmap
  • 🎨Community
    • Website
    • Twitter
    • Telegram
    • Github
  • Sight Incentive Plan - Season 1
  • Sight Incentive Plan - Season 2
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On this page
  • Fully Homomorphic Encryption (FHE)
  • FHE Machine Learning (FHEML)
  1. Overview

FHE

Fully Homomorphic Encryption

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Last updated 9 months ago

Fully Homomorphic Encryption (FHE)

FHE is a cryptographic tool that enables computations to be performed on encrypted data without ever requiring decryption. FHE allows arbitrary computations over ciphertexts, generating an encrypted result that matches the output of operations performed on the plaintext.

FHE Machine Learning (FHEML)

FHEML is the application of FHE to enable privacy-preserving machine learning. FHEML allows data, models and predictions to remain encrypted throughout the ML lifecycle, protecting sensitive information from exposure.

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Performance benchmark from zama.ai