FHE 101

🔍 FHE = Fully Homomorphic Encryption

Fully Homomorphic Encryption (FHE) is an advanced cryptographic paradigm that allows computations to be performed directly on encrypted data, eliminating the need for decryption. This ensures that data remains confidential throughout the entire computation process, significantly enhancing privacy and security in applications such as cloud computing, decentralized networks, and privacy-preserving AI.

For example, in the diagram below, the server can execute computations on encrypted data provided by the client, without ever accessing the raw information. Only the client possesses the decryption key, ensuring that both the original and computed results remain secure and private.

🧬 FHE's Characteristics

FHE has bellow unique key characteristics:

Computation on Encrypted Data: FHE allows computations to be performed directly on encrypted data without decryption. In blockchain, FHE can supports confidential transactions, hiding transaction amounts while still ensuring validity. In AI, FHE enables privacy-preserving machine learning, allowing AI to analyse encrypted data without accessing raw information and be useful for secure federated learning.
End-to-End Privacy: Data owner do not need to share private key and only the data owner can decrypt the final result, neither the computing party nor intermediaries can access raw data. In blockchain, FHE can prevent transaction details from being exposed and protects sensitive business logic. In AI, FHE allows secure AI-as-a-Service and supports privacy-preserving AI inference by ensuring user data remains confidential.
Zero-Trust Computation: FHE Computation can be securely outsourced without trusting the computing entity. In blockchains, FHE enables secure off-chain computation and makes L2 more private and efficient. In AI, FHE supports trustless AI model deployment and enable secure AI collaboration between untrusted parties.
Quantum Safe: FHE harnesses the complexity of lattice-based NP-hard problems to provide strong resistance against quantum attacks. Lattice (like, Shortest Vector Problem, ...) has been actively evaluated from from 2016 and formally accepted by NIST as the mainstream Post Quantum Crypto Standards in 2024.

📜 FHE's History

Fully Homomorphic Encryption (FHE) has evolved from early Homomorphic Encryption (HE) concepts into a powerful tool for performing secure computations on encrypted data without decryption.

🔹 1978 – The Concept is Born Rivest, Adleman, and Dertouzos first proposed the idea of computing on encrypted data without decryption, laying the foundation for homomorphic encryption. Early cryptographic schemes like RSA, ElGamal, and Paillier supported Partial Homomorphic Encryption (PHE), enabling either addition or multiplication—but not both.

🔹 2009 – The First FHE Scheme Craig Gentry introduced the first viable FHE scheme, using ideal lattices and bootstrapping to support unlimited computations on encrypted data. This breakthrough sparked a wave of research into making FHE more practical.

🔹 2011–2017 – Advancements in Efficiency New FHE schemes emerged, including BGV and BFV, which were based on the Learning With Errors (LWE) problem, improving efficiency for real-world applications. The CKKS scheme further expanded FHE’s capabilities by enabling approximate real-number computations, making it particularly valuable for privacy-preserving machine learning.

🔹 2020 – Performance Breakthroughs The TFHE scheme significantly improved FHE performance by accelerating bootstrapping and optimizing ciphertext refresh times. in 2025, ZAMA enhanced its practicality with TFHE‑rs V1.0.0, a production-ready Rust-based implementation that boosted efficiency and usability.

🔹 Mind Network’s Contribution Building on these advancements, Mind Network integrated TFHE‑rs V1 and other FHE library to secure blockchain data processing, marking a major milestone in the real-world deployment of FHE for Web3 security and privacy.

For further insights, refer to wikipedia, Ilaria's talk, Paillier's talk and Gentry's talk.

🏫 FHE's foundational libraries

There are many FHE libraries. Mind Network used and partnered with bellow FHE foundational libraries in research and development:

TFHE-rs: Zama recently released TFHE-rs-v1.0.0 a high-performance production-ready Rust library for FHE based on the TFHE (Torus Fully Homomorphic Encryption) scheme, optimized for fast bootstrapping and efficient bitwise computations. It enables secure processing of encrypted Boolean and small-integer data, making it ideal for privacy-preserving AI inference, confidential blockchain smart contracts, and encrypted decision-making applications. Zama also developed Concrete and Concrete-ML for AI and data scientist as a python-based toolkit.
HEAAN: is a Fully Homomorphic Encryption (FHE) library designed for efficient computations on encrypted real-valued data, making it well-suited for applications in privacy-preserving machine learning, encrypted AI inference, and secure data analytics. Unlike other FHE schemes, HEAAN supports approximate arithmetic, allowing high-precision operations while maintaining performance efficiency. HEAAN is implemented in C++ and adapted to python.
OpenFHE: is a C++ FHE implementing all major schemes (including BFV, BGV) along with bootstrapping and scheme switching. OpenFHE is built with a modular design, enabling high-performance homomorphic operations, secure multiparty computation (MPC), and quantum-resistant encryption. It is actively maintained by leading cryptography researchers and is the successor to PALISADE and HElib.
HEIR: is a compiler toolchain to FHE MLIR (Multi-Level Intermediate Representation) written in Rust. Google developed it with the aim to compile all machine learning code into FHE enabled.
SecretFlow-HEU: is a python based HE library to support multiple types and scalable hardware acceleration. HEU's strong points are the great support on PHE by supporting most PHE algorithms.

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🔍 FHE = Fully Homomorphic Encryption

🧬 FHE's Characteristics

FHE has bellow unique key characteristics:

Computation on Encrypted Data: FHE allows computations to be performed directly on encrypted data without decryption. In blockchain, FHE can supports confidential transactions, hiding transaction amounts while still ensuring validity. In AI, FHE enables privacy-preserving machine learning, allowing AI to analyse encrypted data without accessing raw information and be useful for secure federated learning.

End-to-End Privacy: Data owner do not need to share private key and only the data owner can decrypt the final result, neither the computing party nor intermediaries can access raw data. In blockchain, FHE can prevent transaction details from being exposed and protects sensitive business logic. In AI, FHE allows secure AI-as-a-Service and supports privacy-preserving AI inference by ensuring user data remains confidential.

Zero-Trust Computation: FHE Computation can be securely outsourced without trusting the computing entity. In blockchains, FHE enables secure off-chain computation and makes L2 more private and efficient. In AI, FHE supports trustless AI model deployment and enable secure AI collaboration between untrusted parties.

Quantum Safe: FHE harnesses the complexity of lattice-based NP-hard problems to provide strong resistance against quantum attacks. Lattice (like, Shortest Vector Problem, ...) has been actively evaluated from from 2016 and formally accepted by NIST as the mainstream Post Quantum Crypto Standards in 2024.

📜 FHE's History

Fully Homomorphic Encryption (FHE) has evolved from early Homomorphic Encryption (HE) concepts into a powerful tool for performing secure computations on encrypted data without decryption.

For further insights, refer to wikipedia, Ilaria's talk, Paillier's talk and Gentry's talk.

🏫 FHE's foundational libraries

There are many FHE libraries. Mind Network used and partnered with bellow FHE foundational libraries in research and development:

TFHE-rs: Zama recently released TFHE-rs-v1.0.0 a high-performance production-ready Rust library for FHE based on the TFHE (Torus Fully Homomorphic Encryption) scheme, optimized for fast bootstrapping and efficient bitwise computations. It enables secure processing of encrypted Boolean and small-integer data, making it ideal for privacy-preserving AI inference, confidential blockchain smart contracts, and encrypted decision-making applications. Zama also developed Concrete and Concrete-ML for AI and data scientist as a python-based toolkit.

HEAAN: is a Fully Homomorphic Encryption (FHE) library designed for efficient computations on encrypted real-valued data, making it well-suited for applications in privacy-preserving machine learning, encrypted AI inference, and secure data analytics. Unlike other FHE schemes, HEAAN supports approximate arithmetic, allowing high-precision operations while maintaining performance efficiency. HEAAN is implemented in C++ and adapted to python.

OpenFHE: is a C++ FHE implementing all major schemes (including BFV, BGV) along with bootstrapping and scheme switching. OpenFHE is built with a modular design, enabling high-performance homomorphic operations, secure multiparty computation (MPC), and quantum-resistant encryption. It is actively maintained by leading cryptography researchers and is the successor to PALISADE and HElib.

HEIR: is a compiler toolchain to FHE MLIR (Multi-Level Intermediate Representation) written in Rust. Google developed it with the aim to compile all machine learning code into FHE enabled.

SecretFlow-HEU: is a python based HE library to support multiple types and scalable hardware acceleration. HEU's strong points are the great support on PHE by supporting most PHE algorithms.