Secure genotype imputation using homomorphic encryption

Zhou, Junwei; Lei, Botian; Lang, Huile; Panaousis, Emmanouil; Liang, Kaitai; Xiang, Jianwen

doi:10.1016/j.jisa.2022.103386

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Linear regression has been widely used for tackling secure genome problems (e.g. Aziz et al, 2019;Kim et al, 2018Kim et al, , 2020Blatt et al, 2020;De Cock et al, 2021;Zhou et al, 2023). The reason for this popularity is linked to its arithmetic simplicity and robustness, and track record (e.g.…”

Section: Linear Regression-based Methodsmentioning

confidence: 99%

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption

de Souza,

de Lassus,

Cammarota

2024

Preprint

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Background: Forensic analysis heavily relies on DNA analysis techniques, notably autosomal Single Nucleotide Polymorphisms (SNPs), to expedite the identification of unknown suspects through genomic database searches. However, the uniqueness of an individual’s genome sequence designates it as Personal Identifiable Information (PII), subjecting it to stringent privacy regulations that can impede data access and analysis, as well as restrict the parties allowed to handle the data. Homomorphic Encryption (HE) emerges as a promising solution, enabling the execution of complex functions on encrypted data without the need for decryption. HE not only permits the processing of PII as soon as it is collected and encrypted, such as at a crime scene, but also expands the potential for data processing by multiple entities and artificial intelligence services. Methods: This study introduces HE-based privacy-preserving methods for SNP DNA analysis, offering a means to compute kinship scores for a set of genome queries while meticulously preserving data privacy. We present three distinct approaches, including one unsupervised and two supervised methods, all of which demonstrated exceptional performance in the iDASH 2023 Track 1 competition. Results: Our HE-based methods can rapidly predict 400 kinship scores from an encrypted database containing 2000 entries within seconds, capitalizing on advanced technologies like Intel AVX vector extensions, Intel HEXL, and Microsoft SEAL HE libraries. Crucially, all three methods achieve remarkable accuracy levels (ranging from 96% to 100%), as evaluated by the auROC score metric, while maintaining robust 128-bit security. These findings underscore the transformative potential of HE in both safeguarding genomic data privacy and streamlining precise DNA analysis. Conclusions: Results demonstrate that HE-based solutions can be computationally practical to protect genomic privacy during screening of candidate matches for further genealogy analysis in Forensic Genetic Genealogy (FGG).

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Section: Linear Regression-based Methodsmentioning

confidence: 99%

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption

de Souza,

de Lassus,

Cammarota

2024

Preprint

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“…For example, it has been shown that privacy-enhancing genome-wide association studies (GWAS) can be possible [62,70,13,44]. It has also been shown that secure genotype imputation is feasible and scalable using homomorphic encryption [71,20,36]. Homomorphic encryption was also used for genomic variant querying [19], regression analysis for rare disease variants [68], and inference using genetic variants in machine learning applications [60].…”

Section: Introductionmentioning

confidence: 99%

Ultra-secure storage and analysis of genetic data for the advancement of precision medicine

Blindenbach,

Kang,

Hong

et al. 2024

Preprint

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Cloud computing provides the opportunity to store the ever-growing genotype-phenotype data sets needed to achieve the full potential of precision medicine. However, due to the sensitive nature of this data and the patchwork of data privacy laws across states and countries, additional security protections are proving necessary to ensure data privacy and security. Here we present SQUiD, asecurequeryabledatabase for storing and analyzing genotype-phenotype data. With SQUiD, genotype-phenotype data can be stored in a low-security, low-cost public cloud in the encrypted form, which researchers can securely query without the public cloud ever being able to decrypt the data. We demonstrate the usability of SQUiD by replicating various commonly used calculations such as polygenic risk scores, cohort creation for GWAS, MAF filtering, and patient similarity analysis both on synthetic and UK Biobank data. Our work represents a new and scalable platform enabling the realization of precision medicine without security and privacy concerns.

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Privacy Preserving Genomic Data Imputation using Autoencoders

Pratapa,

Essex

2024

Procedia Computer Science

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Secure genotype imputation using homomorphic encryption

Cited by 6 publications

References 43 publications

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption

Private Detection of Relatives in Forensic Genomics using Homomorphic Encryption

Ultra-secure storage and analysis of genetic data for the advancement of precision medicine

Privacy Preserving Genomic Data Imputation using Autoencoders

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