{"id":50487159,"url":"https://github.com/systemslibrarian/crypto-lab-ckks-lab","last_synced_at":"2026-06-01T23:04:07.435Z","repository":{"id":351102869,"uuid":"1208827345","full_name":"systemslibrarian/crypto-lab-ckks-lab","owner":"systemslibrarian","description":"Browser-based CKKS approximate FHE demo — encrypted floating-point arithmetic, homomorphic neural network inference, rescaling, and the complete FHE trilogy (TFHE + BGV/BFV + CKKS). 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What It Is\n\nCKKS Lab demonstrates CKKS (Cheon-Kim-Kim-Song, ASIACRYPT 2017) — the Fully Homomorphic Encryption scheme for approximate arithmetic on real numbers. Unlike BGV/BFV which produce exact integer results, CKKS deliberately introduces small approximation errors to enable efficient floating-point arithmetic on encrypted data. A CKKS ciphertext encodes a vector of real numbers (n/2 slots for polynomial degree n) and supports vectorized addition and multiplication directly on ciphertexts. CKKS is the FHE scheme behind encrypted machine learning inference — the ability to evaluate a neural network on encrypted input without the server ever seeing the plaintext data.\n\n## 2. When to Use It\n\n- ✅ Encrypted neural network inference on private user data\n- ✅ Encrypted statistics (mean, variance, dot products) on real-valued data\n- ✅ Logistic regression and linear model scoring on encrypted inputs\n- ✅ Privacy-preserving genomics (continuous-valued statistical tests)\n- ❌ Exact integer arithmetic — use BGV or BFV instead\n- ❌ Arbitrary boolean logic — use TFHE instead\n- ❌ Applications requiring exact results (financial, cryptographic) — CKKS approximation error will silently produce wrong answers\n- ❌ Real-time inference on large models — CKKS is significantly slower than plaintext computation (seconds to minutes for production models)\n\n## 3. Live Demo\n\nLink: https://systemslibrarian.github.io/crypto-lab-ckks-lab/\n\nSix exhibits: what CKKS is and why approximation is the right choice for ML, CKKS encode/encrypt/add with approximation error shown honestly, homomorphic multiplication and rescaling with modulus level tracking, encrypted neural network inference end-to-end (2-layer network on encrypted inputs), precision and error accumulation across multiple operations, and the complete FHE trilogy comparison (TFHE + BGV/BFV + CKKS) with decision tree and library guide.\n\n## 4. How to Run Locally\n\n```bash\ngit clone https://github.com/systemslibrarian/crypto-lab-ckks-lab\ncd crypto-lab-ckks-lab\nnpm install\nnpm run dev\n```\n\n## 5. Part of the Crypto-Lab Suite\n\nPart of [crypto-lab](https://systemslibrarian.github.io/crypto-lab/) — browser-based cryptography demos spanning 2,500 years of cryptographic history to NIST FIPS 2024 post-quantum standards.\n\nWhether you eat or drink or whatever you do, do it all for the glory of God. — 1 Corinthians 10:31","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsystemslibrarian%2Fcrypto-lab-ckks-lab","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsystemslibrarian%2Fcrypto-lab-ckks-lab","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsystemslibrarian%2Fcrypto-lab-ckks-lab/lists"}