Private queries on encrypted genomic data

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.’

  • Çetin GS, Chen H, Laine K, Lauter K, Rindal P, Xia Y
    Private queries on encrypted genomic data
    BMC Medical Genomics 2017 10(Suppl 2):45
    (Abstract, PDF)

Secure searching of biomarkers through hybrid homomorphic encryption scheme

The authors ‘propose an efficient method to securely search a matching position with the query data and extract some information at the position. After decryption, only a small amount of comparisons with the query information should be performed in plaintext state. [They] apply this method to find a set of biomarkers in encrypted genomes. The important feature of our method is to encode a genomic database as a single element of [a] polynomial ring.’

  • Kim M, Song Y, Cheon JH
    Secure searching of biomarkers through hybrid homomorphic encryption scheme
    BMC Medical Genomics 2017 10(Suppl 2):42
    (Abstract, PDF)

A privacy-preserving solution for compressed storage and selective retrieval of genomic data

The authors ‘present a privacy-preserving solution named SECRAM (Selective retrieval on Encrypted and Compressed Reference-oriented Alignment Map) for the secure storage of compressed aligned genomic data. Our solution enables selective retrieval of encrypted data and improves the efficiency of downstream analysis (e.g., variant calling).’

  • Huang Z, Ayday E, Lin H, Aiyar RS, Molyneaux A, Xu Z, Fellay J, Steinmetz LM, Hubaux JP
    A privacy-preserving solution for compressed storage and selective retrieval of genomic data
    Genome Research. 2016. Volume 26. Issue 12. Pages 1687-1696.
    (Abstract, PDF, Supplement including source code)

Efficient privacy-preserving string search and an application in genomics

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.’

  • Shimizu K, Nuida K, Rätsch G
    Efficient privacy-preserving string search and an application in genomics
    Bioinformatics. 2016. Volume 32. Issue 11. Pages 1652–1661.
    (Abstract, PDF)

Private genome analysis through homomorphic encryption

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.’

  • Kim M, Lauter K
    Private genome analysis through homomorphic encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S3
    (Abstract, PDF)

Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption

‘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.’

  • Lu WJ, Yamada Y, Sakuma J
    Privacy-preserving genome-wide association studies on cloud environment using fully homomorphic encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S1
    (Abstract, PDF)

Fully outsourced secure genome study based on homomorphic encryption

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.’

  • Zhang Y, Dai W, Jiang X, Xiong H, Wang S
    FORESEE: Fully Outsourced secuRe gEnome Study basEd on homomorphic Encryption
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S5
    (Abstract, PDF)

Secure distributed genome analysis for GWAS and sequence comparison computation

The authors ‘propose techniques for securing computation with real-life genomic data for minor allele frequency and chi-squared statistics computation, as well as distance computation between two genomic sequences, as specified by the iDASH competition tasks. [They] put forward novel optimizations, including a generalization of a version of mergesort, which might be of independent interest.’

  • Zhang Y, Blanton M, Almashaqbeh G
    Secure distributed genome analysis for GWAS and sequence comparison computation
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S4
    (Abstract, PDF)

Privacy-preserving GWAS analysis on federated genomic datasets

The authors ‘present a privacy-preserving GWAS framework on federated genomic datasets. [Their] method is to layer the GWAS computations on top of secure multi-party computation (MPC) systems. This approach allows two parties in a distributed system to mutually perform secure GWAS computations, but without exposing their private data outside.’

  • Constable SD, Tang Y, Wang S, Jiang X, Chapin S
    Privacy-preserving GWAS analysis on federated genomic datasets
    BMC Medical Informatics and Decision Making 2015 15(Suppl 5):S2
    (Abstract, PDF)