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homomorpheR

homomorpheR is privacy-preserving statistics across sites that never share their data. It uses fully homomorphic encryption through the openfhe.R interface to 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 R modeling code — stats4::mle(), stratified survival::coxph(), convex programs via CVXR — run across sites. A frozen implementation of the Paillier additive scheme is kept for backward compatibility.

The version on CRAN is 0.3, the Paillier-only release; this development version is a rewrite on OpenFHE. Install it, with its dependencies, by

remotes::install_github("bnaras/homomorpheR", ref = "v1.0")

The cox and cvxr vignettes also use survival and CVXR, which are suggested rather than imported:

install.packages(c("survival", "CVXR"))

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

Getting started

• introduction — a quick tour of homomorphic computation in R.
• precision — which encrypted computations are exact and which are approximate.
• privacy-preserving-aggregation — exact integer aggregation under BFV.
• query-count-threshold — 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.
• cvxr-cox-lasso-dlbcl — a Cox-lasso fit by consensus ADMM, with CVXR 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

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

Legacy Paillier vignettes. These no longer ship with the package. They are kept in the paillier-archive/ directory of this repository.

• homomorphing — Paillier homomorphic computations.
• DHCox — distributed Cox regression via Paillier.
• QueryNCP — query count with non-cooperating parties.
• DHCoxNCP — distributed Cox with non-cooperating parties.

A related project is distcomp.

Website

You can view everything, including documentation and vignettes on the homomorpheR website.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.