encrypted-computation

PYthon For Homomorphic Encryption Libraries, perform encrypted computations such as sum, mult, scalar product or matrix multiplication in Python, with NumPy compatibility. Uses SEAL/PALISADE as backends, implemented using Cython.

A privacy preserving NLP framework

CoPHEE is a Co-processor for Partially Homomorphic Encrypted Encryption.

Cryptoleq: A Heterogeneous Abstract Machine for Encrypted and Unencrypted Computation.

Pilatus extends the Talos Framework for Android Applications and/or Smartphone-as-Gateway IoT Applications. Pilatus employs partial homomorphic ciphers (Paillier, EC-ElGamal, proxy-re-encryption PRE, order preserving encryption mOPE, key homomorphic)

Build apps that compute on data no one can see, not even the server running them. Agent skills that guide your AI coding agents from privacy idea to a working encrypted application.

Original SEAL homomorphic encryption library, slightly modified to use it as dependency for Pyfhel.

30-day hospital readmission risk scored under fully homomorphic encryption. The patient is analyzed in full. The model reads nothing at all.

Starter kit for encrypted (FHE) computation on the Niobium Fog. Apache-2.0 sample code + public SDK; Fog execution is a hosted service and needs a Niobium account.

Run a neural-network intrusion detector on encrypted network traffic with FHE, executed on the Niobium Fog. Apache-2.0 sample code + public SDK; Fog execution is a hosted service and needs a Niobium account.

Harness and example implementation for the FHE ml-inference workload

Arcium encrypted computation network - MPC node setup and testing

Identify fingerprints on encrypted images using Fully Homomorphic Encryption (Zama Concrete-ML). The server never sees the raw biometric — all ridge detection, enhancement, and feature extraction run directly on ciphertext.

Zama testnet interaction scripts - FHE encrypted smart contracts testing

A privacy-preserving decentralized exchange built on Solana using Arcium's encrypted computation to prevent front-running and MEV attacks. Features client-side encryption, MPC matching, and zero front-running protection.

Browser-based toy BFV integer FHE demo (BGV compared, not run) — homomorphic add/multiply, live noise-budget visualizer, relinearization, coefficient packing (batched add), and real-world deployments. Teaching toy at insecure parameters; unit-tested, nothing faked.