Privacy-preserving genotype imputation in a trusted execution environment

Dokmai, Natnatee; Koçkan, Can; Zhu, Kaiyuan; Şahinalp, S. Cenk; Cho, Hyunghoon

doi:10.1016/j.cels.2021.08.001

Cited by 23 publications

(21 citation statements)

References 46 publications

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“…Threat Model. Gyosa adopts the standard SGX threat model supported by existing research [20,9,17]. We consider a scenario where a client seeks to use sensitive genomic data and perform computation on top of it at third-party infrastructures.…”

Section: Designmentioning

confidence: 99%

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Brito,

Ferreira,

Paulo

2024

Preprint

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Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological insights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to third-party infrastructures for data storage and processing (e.g., cloud computing). Current solutions for data privacy protection resort to centralized designs or cryptographic primitives that impose considerable computational overheads, limiting their applicability to large-scale genomic analysis. We introduce GYOSA, a secure and privacy-preserving distributed genomic analysis solution. Unlike in previous work, GYOSA follows a distributed processing design that enables handling larger amounts of genomic data in a scalable and efficient fashion. Further, by leveraging trusted execution environments (TEEs), namely Intel SGX, GYOSA allows users to confidentially delegate their GWAS analysis to untrusted third-party infrastructures. To overcome the memory limitations of SGX, we implement a computation partitioning scheme within GYOSA. This scheme reduces the number of operations done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, GYOSA provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of GYOSA, reinforcing its ability to provide enhanced security guarantees. Further, the results show that, by distributing GWASes computations, one can achieve a practical and usable privacy-preserving solution.

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Section: Designmentioning

confidence: 99%

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Brito,

Ferreira,

Paulo

2024

Preprint

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“…One of the concerns for creating applications with trusted hardware like SGX is side-channel attacks [17,18,30], where attackers can extract sensitive data by measuring the architectural state during program execution. Therefore, in SECRET-GWAS we develop novel data oblivious GWAS kernels and apply an optimized speculative load hardening to defend against important side-channels such as control flow [16,17], memory access [18][19][20][21], and Spectre [22]. We go into more detail about side-channels in our Threat Model and Challenges sections.…”

Section: Side-channel Mitigationmentioning

confidence: 99%

“…Popular side-channel attacks discussed in prior works such as control flow timing [16,17], memory access pattern analysis [18][19][20][21], as well as Spectre attacks [22] are also part of our threat model. To address this we use a novel combination of oblivious transformations and speculative load hardening to defend against the aforementioned side-channels.…”

Section: Challengesmentioning

confidence: 99%

“…Other Applications using SGX Dokmai et al [17] created SMac, a privacypreserving genotype imputation application using SGX. Researchers often have missing genotypes in their datasets and wish to impute them for their analysis.…”

Section: Mpc and Hementioning

confidence: 99%

“…Our first defense is a data-oblivious implementation of linear and logistic regression kernels. It is well understood that conditional branches and memory accesses dependent on sensitive data can leak private information [16][17][18][19][20][21]. We address this problem by using oblivious code transformations where all sensitive conditional branches are predicated, ensuring that the control flow and memory access sequence are independent of sensitive data input.…”

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confidence: 99%

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SECRET-GWAS: Confidential Computing for Population-Scale GWAS

Rosenblum,

Dong,

Narayanasamy

2024

Preprint

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Genomic data from a single institution lacks global diversity representation, especially for rare variants and diseases. Confidential computing can enable collaborative GWAS without compromising privacy or accuracy, however, due to limited secure memory space and performance overheads previous solutions fail to support widely used regression methods. We present SECRET-GWAS: a rapid, privacy-preserving, population-scale, collaborative GWAS tool. We discuss several system optimizations, including streaming, batching, data parallelization, and reducing trusted hardware overheads to efficiently scale linear and logistic regression to over a thousand processor cores on an Intel SGX-based cloud platform. In addition, we protect SECRET-GWAS against several hardware side-channel attacks, including Spectre, using data-oblivious code transformations and optimized speculative load hardening. SECRET-GWAS is an open-source tool and works with the widely used Hail genomic analysis framework. Our experiments on Azure's Confidential Computing platform demonstrate that SECRET-GWAS enables multivariate linear and logistic regression GWAS queries on population-scale datasets (one million patients, four million SNPs, 12 covariates) from ten independent sources in just 4.5 and 29 minutes, respectively.

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