ArticleBMC medical genomics2018
Secure top most significant genome variants search: iDASH 2017 competition.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Learning-augmented sketching offers improved performance for privacy preserving and secure GWAS.iScience · 2025Article
- Learning-Augmented Sketching Offers Improved Performance for Privacy Preserving and Secure GWAS.bioRxiv : the preprint server for biology · 2024Article
- Federated Analysis for Privacy-Preserving Data Sharing: A Technical and Legal Primer.Annual review of genomics and human genetics · 2023Review
- The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Functional genomics data: privacy risk assessment and technological mitigation.Nature reviews. Genetics · 2022Review
- HySec-Flow: Privacy-Preserving Genomic Computing with SGX-based Big-Data Analytics Framework.Proceedings. IEEE International Conference on Cloud Computing · 2021Article
- Privacy-preserving semi-parallel logistic regression training with fully homomorphic encryption.BMC medical genomics · 2020Article
- iDASH secure genome analysis competition 2017.BMC medical genomics · 2018Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
backgroundOne of the 3 tracks of iDASH Privacy & Security Workshop 2017 competition was to execute a whole genome variants search on private genomic data. Particularly, the search application was to find the top most significant SNPs (Single-Nucleotide Polymorphisms) in a database of genome records labeled with control or case. In this paper we discuss the solution submitted by our team to this competition.
methodsPrivacy and confidentiality of genome data had to be ensured using Intel SGX enclaves. The typical use-case of this application is the multi-party computation (each party possessing one or several genome records) of the SNPs which statistically differentiate control and case genome datasets.
resultsOur solution consists of two applications: (i) compress and encrypt genome files and (ii) perform genome processing (top most important SNPs search). We have opted for a horizontal treatment of genome records and heavily used parallel processing. Rust programming language was employed to develop both applications.
conclusionsExecution performance of the processing applications scales well and very good performance metrics are obtained. Contest organizers selected it as the best submission amongst other received competition entries and our team was awarded the first prize on this track.
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