MaskAl: Privacy Preserving Masked Reads Alignment using Intel SGX

Lambert, Christoph; Fernandes, Maria; Decouchant, Jérémie; Esteves-Veríssimo, Paulo

doi:10.1109/srds.2018.00022

Cited by 14 publications

(10 citation statements)

References 42 publications

(60 reference statements)

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“…We stress that HySec-Flow can be easily adapted to a category of many genomics applications where the algorithms are pleasantly data-parallel, e.g., for genome variation calling [9], [41], for gene expression analysis using RNA-seq data [42], and peptide identification in clinical proteomics [43]. However, in each of these cases, we need to devise a customized data partition algorithm that can assemble subsets of input data for subtasks so that the subtasks are performed most efficiently.…”

Section: Discussionmentioning

confidence: 99%

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

Widanage¹,

Liu²,

Li³

et al. 2021

Preprint

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Trusted execution environments (TEE) such as Intel's Software Guard Extension (SGX) have been widely studied to boost security and privacy protection for the computation of sensitive data such as human genomics. However, a performance hurdle is often generated by SGX, especially from the small enclave memory. In this paper, we propose a new Hybrid Secured Flow framework (called "HySec-Flow") for large-scale genomic data analysis using SGX platforms. Here, the data-intensive computing tasks can be partitioned into independent subtasks to be deployed into distinct secured and non-secured containers, therefore allowing for parallel execution while alleviating the limited size of Page Cache (EPC) memory in each enclave. We illustrate our contributions using a workflow supporting indexing, alignment, dispatching, and merging the execution of SGX-enabled containers. We provide details regarding the architecture of the trusted and untrusted components and the underlying Scorn and Graphene support as generic shielding execution frameworks to port legacy code. We thoroughly evaluate the performance of our privacy-preserving reads mapping algorithm using real human genome sequencing data. The results demonstrate that the performance is enhanced by partitioning the time-consuming genomic computation into subtasks compared to the conventional execution of the data-intensive reads mapping algorithm in an enclave. The proposed HySec-Flow framework is made available as an open-source and adapted to the data-parallel computation of other large-scale genomic tasks requiring security and scalable computational resources.

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Section: Discussionmentioning

confidence: 99%

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

Widanage¹,

Liu²,

Li³

et al. 2021

Preprint

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“…BALAUR uses a lot of memory and requires substantial network bandwidth [49]. While Zhao et al [46] created an algorithm that works on short reads, most modern sequencers produce long reads which might render this approach to be less beneficial for real-world usage.…”

Section: Critical Analysismentioning

confidence: 99%

Digital DNA lifecycle security and privacy: an overview

Alsaffar

Hasan²,

McStay

et al. 2022

Briefings in Bioinformatics

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DNA sequencing technologies have advanced significantly in the last few years leading to advancements in biomedical research which has improved personalised medicine and the discovery of new treatments for diseases. Sequencing technology advancement has also reduced the cost of DNA sequencing, which has led to the rise of direct-to-consumer (DTC) sequencing, e.g. 23andme.com, ancestry.co.uk, etc. In the meantime, concerns have emerged over privacy and security in collecting, handling, analysing and sharing DNA and genomic data. DNA data are unique and can be used to identify individuals. Moreover, those data provide information on people’s current disease status and disposition, e.g. mental health or susceptibility for developing cancer. DNA privacy violation does not only affect the owner but also affects their close consanguinity due to its hereditary nature. This article introduces and defines the term ‘digital DNA life cycle’ and presents an overview of privacy and security threats and their mitigation techniques for predigital DNA and throughout the digital DNA life cycle. It covers DNA sequencing hardware, software and DNA sequence pipeline in addition to common privacy attacks and their countermeasures when DNA digital data are stored, queried or shared. Likewise, the article examines DTC genomic sequencing privacy and security.

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“…Isolates have been used to protect a zoo of computations of interest, e.g., ML [20,54,72,83,85] and genomic computations [21,56,59], and have been used to emulate or speed up cryptographic techniques such as functional encryption [36] and secure multi-party computations [35,40,76]. These can be seen as use-cases, specialized with a particular policy and program, of Veracruz.…”

Section: Related Workmentioning

confidence: 99%

Private delegated computations using strong isolation

Brossard¹,

Bryant²,

El³

et al. 2022

Preprint

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Sensitive computations are now routinely delegated to thirdparties. In response, Confidential Computing technologies are being introduced to microprocessors, offering a protected processing environment, which we generically call an isolate, providing confidentiality and integrity guarantees to code and data hosted within-even in the face of a privileged attacker. Isolates, with an attestation protocol, permit remote third-parties to establish a trusted "beachhead" containing known code and data on an otherwise untrusted machine. Yet, the rise of these technologies introduces many new problems, including: how to ease provisioning of computations safely into isolates; how to develop distributed systems spanning multiple classes of isolate; and what to do about the billions of "legacy" devices without support for Confidential Computing?Tackling the problems above, we introduce Veracruz, a framework that eases the design and implementation of complex privacy-preserving, collaborative, delegated computations among a group of mutually mistrusting principals. Veracruz supports multiple isolation technologies and provides a common programming model and attestation protocol across all of them, smoothing deployment of delegated computations over supported technologies. We demonstrate Veracruz in operation, on private in-cloud object detection on encrypted video streaming from a video camera. In addition to supporting hardware-backed isolates-like AWS Nitro Enclaves and Arm ® Confidential Computing Architecture Realms-Veracruz also provides pragmatic "software isolates" on Armv8-A devices without hardware Confidential Computing capability, using the high-assurance seL4 microkernel and our IceCap framework.

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MaskAl: Privacy Preserving Masked Reads Alignment using Intel SGX

Cited by 14 publications

References 42 publications

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework

Digital DNA lifecycle security and privacy: an overview

Private delegated computations using strong isolation

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