Biology subjects

Dokmai, N.

Publications and source records attributed to Dokmai, N..

4 recordsLinked to original sources

bioRxiv2024

Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS

The introduction of trusted execution environments (TEEs), such as secure enclaves provided by the Intel SGX technology has enabled secure and privacy-preserving computation on the cloud. The stringent resource limitations, such as memory constraints, required by some TEEs necessitates the development of computational approaches with reduced memory usage, such as sketching. One example is the SkSES method for GWAS on a cohort of case and control samples from multiple institutions, which identifies the most significant SNPs in a privacy-preserving manner without disclosing sensitive genotype information to other institutions or the cloud service provider. Here we show how to improve the performance of SkSES on large datasets by augmenting it with a learning-augmented approach. Specifically, we show how individual institutions can perform smaller scale GWAS on their own datasets and identify two sets of variants according to certain criteria, which are then used to guide the sketching process to more accurately identify significant variants over the collective dataset. The new method achieves up to 40% accuracy gain compared to the original SkSES method under the same memory constraints on datasets we tested on. The code is available at https://github.com/alreadydone/sgx-genome-variants-search. Contactcenk.sahinalp@nih.gov

bioinformatics↗

bioRxiv2024

TX-Phase: Secure Phasing of Private Genomes in a Trusted Execution Environment

Genotype imputation servers enable researchers with limited resources to extract valuable insights from their data with enhanced accuracy and ease. However, the utility of these services is limited for those with sensitive study cohorts or those in restrictive regulatory environments due to data privacy concerns. Although privacy-preserving analysis tools have been developed to broaden access to these servers, none of the existing methods support haplotype phasing, a critical component of the imputation workflow. The complexity of phasing algorithms poses a significant challenge in maintaining practical performance under privacy constraints. Here, we introduce TX-Phase, a secure haplotype phasing method based on the framework of Trusted Execution Environments (TEEs). TX-Phase allows users private genomic data to be phased while ensuring data confidentiality and integrity of the computation. We introduce novel data-oblivious algorithmic techniques based on compressed reference panels and dynamic fixed-point arithmetic that comprehensively mitigate side-channel leakages in TEEs to provide robust protection of users genomic data throughout the analysis. Our experiments on a range of datasets from the UK Biobank and Haplotype Reference Consortium demonstrate the state-of-the-art phasing accuracy and practical runtimes of TX-Phase. Our work enables secure phasing of private genomes, opening access to large reference genomic datasets for a broader scientific community.

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bioRxiv2021

Privacy-Preserving Genotype Imputation in a Trusted Execution Environment

Genotype imputation is an essential tool in genetics research, whereby missing genotypes are inferred based on a panel of reference genomes to enhance the power of downstream analyses. Recently, public imputation servers have been developed to allow researchers to leverage increasingly large-scale and diverse genetic data repositories for imputation. However, privacy concerns associated with uploading ones genetic data to a third-party server greatly limit the utility of these services. In this paper, we introduce a practical, secure hardware-based solution for a privacy-preserving imputation service, which keeps the input genomes private from the service provider by processing the data only within a Trusted Execution Environment (TEE) offered by the Intel SGX technology. Our solution features SMac, an efficient, side-channel-resilient imputation algorithm designed for Intel SGX, which employs the hidden Markov model (HMM)-based imputation strategy also utilized by a state-of-the-art imputation software Minimac. SMac achieves imputation accuracies virtually identical to those of Minimac and provides protection against known attacks on SGX while maintaining scalability to large datasets. We additionally show the necessity of our strategies for mitigating side-channel risks by identifying vulnerabilities in existing imputation software and controlling their information exposure. Overall, our work provides a guideline for practical and secure implementation of genetic analysis tools in SGX, representing a step toward privacy-preserving analysis services that can facilitate data sharing and accelerate genetics research.{dagger} AvailabilityOur software is available at https://github.com/ndokmai/sgx-genotype-imputation.

bioinformatics↗

bioRxiv2018

Sketching Algorithms for Genomic Data Analysis and Querying in a Secure Enclave

Current practices in collaborative genomic data analysis (e.g. PCAWG [1]) necessitate all involved parties to exchange individual patient data and perform all analysis locally, or use a trusted server for maintaining all data to perform analysis in a single site (e.g. the Cancer Genome Collaboratory). Since both approaches involve sharing genomic sequence data - which is typically not feasible due to privacy issues, collaborative data analysis remains to be a rarity in genomic medicine.\n\nIn order to facilitate efficient and effective collaborative or remote genomic computation we introduce SkSES (Sketching algorithms for Secure Enclave based genomic data analysiS), a computational framework for performing data analysis and querying on multiple, individually encrypted genomes from several institutions in an untrusted cloud environment. Unlike other techniques for secure/privacy preserving genomic data analysis, which typically rely on sophisticated cryptographic techniques with prohibitively large computational overheads, SkSES utilizes the secure enclaves supported by current generation microprocessor architectures such as Intels SGX. The key conceptual contribution of SkSES is its use of sketching data structures that can fit in the limited memory available in a secure enclave.\n\nWhile streaming/sketching algorithms have been developed for many applications in computer science, their feasibility in genomics has remained largely unexplored. On the other hand, even though privacy and security issues are becoming critical in genomic medicine, available cryptographic techniques based on, e.g. homomorphic encryption or garbled circuits, fail to address the performance demands of this rapidly growing field. The alternative offered by Intels SGX, a combination of hardware and software solutions for secure data analysis, is severely limited by the relatively small size of a secure enclave, a private region of the memory protected from other processes. SkSES addresses this limitation through the use of sketching data structures to support efficient secure and privacy preserving SNP analysis across individually encrypted VCF files from multiple institutions. In particular SkSES provides the users the ability to query for the \"k most significant SNPs\" among any set of user specified SNPs and any value of k - even when the total number of SNPs to be maintained is far beyond the memory capacity of the secure enclave.\n\nResultsWe tested SkSES on the complete iDASH-2017 competition data set comprised of 1000 case and 1000 control samples related to an unknown phenotype. SkSES was able to identify the top SNPs with respect to the{chi} 2 statistic, among any user specified subset of SNPs across this data set of 2000 individually encrypted complete human genomes quickly and accurately - demonstrating the feasibility of secure and privacy preserving computation for genomic medicine via Intels SGX.\n\nAvailabilityhttps://github.com/ndokmai/sgx-genome-variants-search

bioinformatics↗