In this preprint, Bonte et al. describe both a homomorphic encryption approach and a secure multiparty computation approach and provide efficient implementations.
The authors ‘present a novel privacy-preserving algorithm for fully outsourcing the storage of large genomic data files to a public cloud and enabling researchers to efficiently search for variants of interest. In order to protect data and query confidentiality from possible leakage, [their] solution exploits optimal encoding for genomic variants and combines it with homomorphic encryption and private information retrieval.’
The authors ‘propose Fhe-Bloom and Phe-Bloom, two efficient approaches for genetic disease testing using homomorphically encrypted Bloom filters. Both approaches allow the data owner to securely outsource storage and computation to an untrusted cloud. Fhe-Bloom is fully secure in the semi-honest model while Phe-Bloom slightly relaxes security guarantees in a trade-off for highly improved performance.’
Çetin et al. present ‘a novel string matching protocol to enable privacy-preserving queries on homomorphically encrypted data. [Their] protocol combines state-of-the-art techniques from homomorphic encryption and private set intersection protocols to minimize the computational and communication cost.’
The authors ‘propose a novel approach that combines efficient string data structures such as the Burrows–Wheeler transform with cryptographic techniques based on additive homomorphic encryption. [They] assume that the sequence data is searchable in efficient iterative query operations over a large indexed dictionary, for instance, from large genome collections and employing the (positional) Burrows–Wheeler transform. [They] use a technique called oblivious transfer that is based on additive homomorphic encryption to conceal the sequence query and the genomic region of interest in positional queries.’
The authors ‘present evaluation algorithms for secure computation of the minor allele frequencies and chi square statistic in a genome-wide association studies setting. [They] also describe how to privately compute the Hamming distance and approximate Edit distance between encrypted DNA sequences. Finally, [they] compare performance details of using two practical homomorphic encryption schemes – the BGV scheme by Gentry, Halevi and Smart and the YASHE scheme by Bos, Lauter, Loftus and Naehrig.’
‘To maintain the privacy of subjects’, the authors ‘propose encryption of all genotype and phenotype data. To allow the cloud to perform meaningful computation in relation to the encrypted data, [they] use a fully homomorphic encryption scheme. Noting that [they] can evaluate typical statistics for GWAS from a frequency table, [their] solution evaluates frequency tables with encrypted genomic and clinical data as input. [They] propose to use a packing technique for efficient evaluation of these frequency tables.’
The authors ‘present a novel framework (FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption) to fully outsource GWAS (i.e., chi-square statistic computation) using homomorphic encryption. The proposed framework enables secure divisions over encrypted data. [They] introduce two division protocols (i.e., secure errorless division and secure approximation division) with a trade-off between complexity and accuracy in computing chi-square statistics.’
In this Nature News piece, Hayden explores the potential of homomorphic encryption to process genetic data stored in the cloud.
Lu et al. ‘propose encryption of all genotype and phenotype data. To allow the cloud to perform meaningful computation in relation to the encrypted data, [they] use a fully homomorphic encryption scheme. Noting that [they] can evaluate typical statistics for GWAS from a frequency table, [their] solution evaluates frequency tables with encrypted genomic and clinical data as input. [They] propose to use a packing technique for efficient evaluation of these frequency tables.’