References of "Lambert, Christoph 50026839"
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See detailMaskAl: Privacy Preserving Masked Reads Alignment using Intel SGX
Lambert, Christoph UL; Fernandes, Maria UL; Decouchant, Jérémie UL et al

Scientific Conference (2018)

The recent introduction of new DNA sequencing techniques caused the amount of processed and stored biological data to skyrocket. In order to process these vast amounts of data, bio-centers have been ... [more ▼]

The recent introduction of new DNA sequencing techniques caused the amount of processed and stored biological data to skyrocket. In order to process these vast amounts of data, bio-centers have been tempted to use low-cost public clouds. However, genomes are privacy sensitive, since they store personal information about their donors, such as their identity, disease risks, heredity and ethnic origin. The first critical DNA processing step that can be executed in a cloud, i.e., read alignment, consists in finding the location of the DNA sequences produced by a sequencing machine in the human genome. While recent developments aim at increasing performance, only few approaches address the need for fast and privacy preserving read alignment methods. This paper introduces MaskAl, a novel approach for read alignment. MaskAl combines a fast preprocessing step on raw genomic data — filtering and masking — with established algorithms to align sanitized reads, from which sensitive parts have been masked out, and refines the alignment score using the masked out information with Intel’s software guard extensions (SGX). MaskAl is a highly competitive privacy-preserving read alignment software that can be massively parallelized with public clouds and emerging enclave clouds. Finally, MaskAl is nearly as accurate as plain-text approaches (more than 96% of aligned reads with MaskAl compared to 98% with BWA) and can process alignment workloads 87% faster than current privacy-preserving approaches while using less memory and network bandwidth. [less ▲]

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See detailEnclave-Based Privacy-Preserving Alignment of Raw Genomic Information
Volp, Marcus UL; Decouchant, Jérémie UL; Lambert, Christoph UL et al

Scientific Conference (2017, October)

Recent breakthroughs in genomic sequencing led to an enormous increase of DNA sampling rates, which in turn favored the use of clouds to e ciently process huge amounts of genomic data. However, while ... [more ▼]

Recent breakthroughs in genomic sequencing led to an enormous increase of DNA sampling rates, which in turn favored the use of clouds to e ciently process huge amounts of genomic data. However, while allowing possible achievements in personalized medicine and related areas, cloud-based processing of genomic information also entails signi cant privacy risks, asking for increased protection. In this paper, we focus on the rst, but also most data-intensive, processing step of the genomics information processing pipeline: the alignment of raw genomic data samples (called reads) to a synthetic human reference genome. Even though privacypreserving alignment solutions (e.g., based on homomorphic encryption) have been proposed, their slow performance encourages alternatives based on trusted execution environments, such as Intel SGX, to speed up secure alignment. Such alternatives have to deal with data structures whose size by far exceeds secure enclave memory, requiring the alignment code to reach out into untrusted memory. We highlight how sensitive genomic information can be leaked when those enclave-external alignment data structures are accessed, and suggest countermeasures to prevent privacy breaches. The overhead of these countermeasures indicate that the competitiveness of a privacy-preserving enclavebased alignment has yet to be precisely evaluated. [less ▲]

Detailed reference viewed: 89 (12 UL)