A server-side CKKS GPU library fully interoperable with OpenFHE.
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Updated
Jul 14, 2026 - Cuda
A server-side CKKS GPU library fully interoperable with OpenFHE.
Open-source FHE client and toolchain. Build fully homomorphic encryption applications with the nb DSL, instrumented OpenFHE, FHETCH API or a CUDA-style library API, record one Polynomial IR trace, and deploy to the Niobium accelerator
Harness and example implementation the FHE fetch-by-similarity workload
A Skill for Anthropic Claude that enables Claude users to more easily create secure computation applications that use fully homomorphic encryption. The initial commit demonstrates an 11% improvement over non-skill Claude in satisfying application goals, at a cost increase of roughly 50% in tokens on the test suite.
Unified attack-replay regression harness for FHE libraries (SEAL, OpenFHE, Lattigo, tfhe-rs).
Reference implementation of the BHDR regression kernel: BSGS-hoisted diagonal Kernel SHAP regression under CKKS FHE.
Drop-in encrypted Fairlearn metrics over CKKS. Same API surface; ciphertext arithmetic via TenSEAL or OpenFHE.
FHE Oracle — adversarial precision testing for Fully Homomorphic Encryption. Open-source edition (AGPL-3.0).
Docker base image with pre-built OpenFHE libraries for creating language bindings and applications. Ready-to-use development environment for homomorphic encryption projects.
A Benchmarking Framework for Fully Homomorphic Encryption Libraries
Proxy simulation for evaluating encrypted LLM accuracy without running full CKKS inference. IIT Big Data X REU 2025, eScience 2025.
The Goddess of Encryption Ecosystem
Application of Homomorphic Encryption for Financial Services
GPU-accelerated homomorphic blind vector recall for OpenFHE CKKS via FIDESlib. Encrypted cosine recall from minutes to single-digit seconds per query, bit-exact, with the secret key never on the GPU.
Research prototype for real Mamba-2-130M inference under CKKS/FHE (OpenFHE/FIDESlib-GPU): +0.12% PPL surrogate and a verified 24-layer, 3-token encrypted B300 path.
Privacy-preserving neural networks in C++ using OpenFHE. Analyzes performance impact (runtime/memory) on encrypted training and inference.
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