homomorphepy

homomorphepy is privacy-preserving statistics across sites that never share their data. It uses fully homomorphic encryption through the openfhe-python binding of OpenFHE — CKKS for real-valued arithmetic, BFV and BGV for exact integers — with n-of-n threshold key generation so that no single party can decrypt. On top of these it ships master/worker primitives that let ordinary Python modeling code — scipy.optimize.minimize(), stratified Cox partial likelihoods from statsmodels, convex programs via cvxpy — run across sites.

Version 1.0, wrapping OpenFHE 1.5.1. Install it by

pip install homomorphepy

The cox and cox-lasso pages also use statsmodels and cvxpy, which are optional rather than required:

pip install "homomorphepy[stats]"

openfhe is an optional dependency because its published wheels are tagged py3-none-any while containing Linux/CPython-3.12 binaries — pip install reports success on macOS and the import then fails. See the README for the working paths.

The pages build up from a gentle introduction to complete distributed protocols:

Getting started

• introduction — a quick tour of homomorphic computation in Python.
• precision — which encrypted computations are exact and which are approximate.
• aggregation — exact integer aggregation under BFV.
• query-count — a count across sites under threshold keys.
• mle — homomorphic maximum-likelihood estimation for a Poisson parameter.

Distributed statistical modeling under FHE

• cox — stratified Cox regression distributed across sites under CKKS.
• cox-threshold — the same fit under n-of-n threshold key generation, so no single party can decrypt.
• cox-lasso — a Cox-lasso fit by consensus ADMM, with cvxpy at each site, under threshold FHE on the DLBCL gene-expression data.
• secure-inference — two-party encrypted prediction.
• encrypted-regression — logistic regression on encrypted data via a Chebyshev sigmoid approximation.
• similarity — federated cosine-similarity retrieval with site-private fine-tuned models.

Gaussian-noise variants

• dp, consensus-admm-dp — the threshold-FHE protocols above with site-side Gaussian noise. Demonstrations, not a privacy guarantee.

Source

The code is on GitHub.

This page was rendered with the crypto backend available. Every number on these pages was computed through real encryption.