Paper 2017/326

Labeled Homomorphic Encryption: Scalable and Privacy-Preserving Processing of Outsourced Data

Manuel Barbosa, Dario Catalano, and Dario Fiore

Abstract

We consider the problem of privacy-preserving processing of outsourced data, where a Cloud server stores data provided by one or multiple data providers and then is asked to compute several functions over it. We propose an efficient methodology that solves this problem with the guarantee that a honest-but-curious Cloud learns no information about the data and the receiver learns nothing more than the results. Our main contribution is the proposal and efficient instantiation of a new cryptographic primitive called Labeled Homomorphic Encryption (labHE). The fundamental insight underlying this new primitive is that homomorphic computation can be significantly accelerated whenever the program that is being computed over the encrypted data is known to the decrypter and is not secret---previous approaches to homomorphic encryption do not allow for such a trade-off. Our realization and implementation of labHE targets computations that can be described by degree-two multivariate polynomials, which capture an important range of statistical functions. As a specific application, we consider the problem of privacy preserving Genetic Association Studies (GAS), which require computing risk estimates for given traits from statistically relevant features in the human genome. Our approach allows performing GAS efficiently, non interactively and without compromising neither the privacy of patients nor potential intellectual property that test laboratories may want to protect.