Fully homomorphic encryption enables secure statistical modeling across sites
Fully Homomorphic Encryption for Statistical Modeling
Cryptography and Security
Summary
Sometimes, different groups want to analyze combined data but can't share their raw information because it's private. The authors show how to do statistics on encrypted data that stays secret the whole time, using tools they built in R, a popular statistics programming language. They demonstrate this with examples like a survival model and a method that picks important factors without revealing the data. This means many parties can work together on data analysis without exposing private information, assuming everyone follows rules to keep their secrets safe.
What this means in practice
- •For healthcare data teams: Support collaborative survival analysis on encrypted patient data across multiple hospitals without sharing raw records.
- •For financial risk analysts: Conduct joint risk modeling on encrypted financial datasets held by different firms maintaining data privacy.
Authors
Balasubramanian Narasimhan
Abstract
Fully homomorphic encryption allows arithmetic to be carried out on encrypted values, so that a party performing a computation need not see the data it operates on. This is useful wherever collaborating parties or sites cannot share data records or computed summaries yet need to do a joint analysis. We demonstrate the protocols needed to perform such analyses reproducibly via two packages in R, a platform widely used for applied statistics. The first, openfhe.R, is an interface to the OpenFHE C++ library, which exposes exact integer arithmetic (BFV, BGV), approximate real-valued arithmetic (CKKS), Boolean circuits, and multiparty key generation. The second is homomorpheR, which builds a small set of master/worker primitives for multi-party protocols on top of the first. Together, the two let an ordinary R statistical routine compose with an encrypted-arithmetic layer at the function-value boundary, provided the quantity crossing that boundary decomposes as a sum over sites. Two validation studies are presented: distributed stratified Cox regression, where stats4::mle() converges through an encrypted channel, and federated Cox-lasso via convex optimization using consensus ADMM, where the cross-site update is the only encrypted step. We also discuss federated similarity retrieval and two-party prediction. Throughout, the parties are assumed honest-but-curious, and threshold key generation removes the assumption that any single party holds a usable secret key.