OpenFHE

open_source

OpenFHE is an open-source FHE library supporting BGV, BFV, CKKS, FHEW, and TFHE post-quantum encryption schemes for privacy-preserving computation.

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About

OpenFHE is a community-driven, open-source Fully Homomorphic Encryption (FHE) library that enables computation directly on encrypted data — without ever decrypting it. It provides efficient and extensible implementations of all major post-quantum FHE schemes: BGV, BFV, CKKS, DM (FHEW), and CGGI (TFHE), making it one of the most comprehensive FHE libraries available. Designed with usability and performance in mind, OpenFHE features simplified APIs, a modular architecture, cross-platform support, and integration with hardware accelerators. It complies with the HomomorphicEncryption.org post-quantum security standards, making it a reliable choice for security-critical applications. The library was created by authors of prominent prior FHE projects including PALISADE, HElib, HEAAN, and FHEW, incorporating best design patterns from those systems alongside novel concepts. Generously supported by DARPA and formally affiliated with NumFOCUS, OpenFHE has an active developer community and hosts regular webinars. Use cases include privacy-preserving machine learning, secure cloud computing, encrypted medical data analysis, confidential multi-party computation, and regulatory-compliant data processing. With a permissive BSD 2-clause license, OpenFHE is easy to wrap and redistribute within commercial products. OpenFHE is ideal for cryptography researchers, security engineers, and developers building applications that require strong data privacy guarantees without sacrificing computational capability.

Key Features

Comprehensive FHE Scheme Support: Supports all major Fully Homomorphic Encryption schemes including BGV, BFV, CKKS, DM (FHEW), and CGGI (TFHE) under one unified library.
Post-Quantum Security Compliance: Fully compliant with HomomorphicEncryption.org post-quantum security standards, ensuring future-proof cryptographic protection.
Hardware Accelerator Integration: Designed to integrate with hardware accelerators to boost performance in computationally intensive FHE operations.
Bootstrapping Support: Includes multiple bootstrapping designs — a key FHE operation for enabling unlimited computation depth — with more efficient methods in active development.
Modular, Cross-Platform Architecture: Features a clean, modular design with simplified APIs and cross-platform support, making it straightforward to embed in diverse environments and products.

Use Cases

Privacy-preserving machine learning — training and inferencing on encrypted datasets without exposing raw data to the compute environment.
Confidential cloud computing — offloading sensitive computations to untrusted cloud servers while keeping data encrypted end-to-end.
Encrypted medical data analysis — running statistical or AI workloads on patient records without ever decrypting them, maintaining HIPAA-level privacy.
Secure multi-party computation — enabling multiple parties to jointly compute on their combined private data without revealing individual inputs.
Regulatory-compliant data processing — meeting stringent data privacy regulations (GDPR, HIPAA, etc.) by ensuring sensitive information never appears in plaintext during processing.

Pros

Permissive Open-Source License: Released under the BSD 2-clause license, allowing easy integration, wrapping, and redistribution in both open-source and commercial products.
Designed by Leading FHE Researchers: Built by the authors of PALISADE, HElib, HEAAN, and FHEW, incorporating best practices from the most influential FHE projects in the field.
Active Community and Support: Backed by DARPA, affiliated with NumFOCUS, and supported by a vibrant community offering forums, mailing lists, and regular educational webinars.
Broadest FHE Scheme Coverage: One of the only libraries to support all leading FHE schemes in a single, unified codebase with consistent APIs.

Cons

Steep Learning Curve: Fully Homomorphic Encryption is an advanced cryptographic concept; users without a background in cryptography or mathematics may find it difficult to use effectively.
No Web UI or Managed Service: OpenFHE is a software library with no SaaS or hosted offering, requiring users to set up, compile, and manage their own infrastructure.
Inherent FHE Performance Overhead: Like all FHE libraries, operations on encrypted data are significantly slower than plaintext equivalents, limiting practical use cases without specialized hardware.

Frequently Asked Questions

FHE is a form of encryption that allows computations to be performed directly on encrypted data without decrypting it first, so the results, once decrypted, match what would have been produced on the plaintext. This enables truly privacy-preserving data processing.

OpenFHE supports the BGV, BFV, CKKS, DM (FHEW), and CGGI (TFHE) schemes — covering all major FHE paradigms including leveled, bootstrapped, and Boolean circuit approaches.

OpenFHE is released under the BSD 2-clause open-source license, which is permissive and allows integration and redistribution in both open-source and commercial products.

OpenFHE was created by some of the authors of PALISADE, HElib, HEAAN, and FHEW. It is maintained by a diverse community of contributors, supported by DARPA, and affiliated with NumFOCUS.

Yes. OpenFHE is designed for both research and production, with post-quantum security standards compliance, hardware accelerator support, and a permissive license that allows commercial deployment.