DNA-SeAl: Sensitivity Levels to Optimize the Performance of Privacy-Preserving DNA Alignment

• Published in: IEEE journal of biomedical and health informatics 2020-03, Vol.24 (3), p.907-915
• Main Authors: Fernandes, Maria, Decouchant, Jeremie, Volp, Marcus, Couto, Francisco M., Esteves-Verissimo, Paulo
• Format: Article
• Language: English
• Subjects: Algorithms; Alignment; Bioinformatics; Classification; Cloud computing; Computer Security; Confidentiality; Cryptography; Data privacy; Databases, Genetic; Deoxyribonucleic acid; DNA; DNA - analysis; DNA - genetics; DNA sequencing; Genomes; Genomics; High-Throughput Nucleotide Sequencing; Humans; Levels; Next-generation sequencing; Nucleotide sequence; Parallel processing; Polymorphism, Single Nucleotide - genetics; Privacy; Risk analysis; Risk assessment; Security; Sensitivity; Sequence Alignment - methods; Sequence Analysis, DNA - methods; Sequential analysis
• ISSN: 2168-2194; 2168-2208
• DOI: 10.1109/JBHI.2019.2914952

The advent of next-generation sequencing (NGS) machines made DNA sequencing cheaper, but also put pressure on the genomic life-cycle, which includes aligning millions of short DNA sequences, called reads, to a reference genome. On the performance side, efficient algorithms have been developed, and parallelized on public clouds. On the privacy side, since genomic data are utterly sensitive, several cryptographic mechanisms have been proposed to align reads more securely than the former, but with a lower performance. This paper presents DNA-SeAl a novel contribution to improving the privacy × performance product in current genomic workflows. First, building on recent works that argue that genomic data needs to be treated according to a threat-risk analysis, we introduce a multi-level sensitivity classification of genomic variations designed to prevent the amplification of possible privacy attacks. We show that the usage of sensitivity levels reduces future re-identification risks, and that their partitioning helps prevent linkage attacks. Second, after extending this classification to reads, we show how to align and store reads using different security levels. To do so, DNA-SeAl extends a recent reads filter to classify unaligned reads into sensitivity levels, and adapts existing alignment algorithms to the reads sensitivity. We show that using DNA-SeAl allows high performance gains whilst enforcing high privacy levels in hybrid cloud environments.