Privacy-preserving storage of sequenced genomic data

Abstract

Background The current and future applications of genomic data may raise ethical and privacy concerns. Processing and storing of this data introduce a risk of abuse by potential offenders since the human genome contains sensitive personal information. For this reason, we have developed a privacy-preserving method, named Varlock providing secure storage of sequenced genomic data. We used a public set of population allele frequencies to mask the personal alleles detected in genomic reads. Each personal allele described by the public set is masked by a randomly selected population allele with respect to its frequency. Masked alleles are preserved in an encrypted confidential file that can be shared in whole or in part using public-key cryptography. Results Our method masked the personal variants and introduced new variants detected in a personal masked genome. Alternative alleles with lower population frequency were masked and introduced more often. We performed a joint PCA analysis of personal and masked VCFs, showing that the VCFs between the two groups cannot be trivially mapped. Moreover, the method is reversible and personal alleles in specific genomic regions can be unmasked on demand. Conclusion Our method masks personal alleles within genomic reads while preserving valuable non-sensitive properties of sequenced DNA fragments for further research. Personal alleles in the desired genomic regions may be restored and shared with patients, clinics, and researchers. We suggest that the method can provide an additional security layer for storing and sharing of the raw aligned reads.

Data Security and Privacy in Genomics Research: A Comparative Analysis to Protect Confidentiality

“…Moreover, sensitive information like prosperity records, innate data, and sexual presentation can be used to isolate, constrain, or harm individuals. (Hekel et al, 2021) In this way, it is basic to ensure that personal data is collected, put absent, and shared securely and with consent. • Essential for national security and monetary soundness.…”

Future-proofing genomic data and consent management: a comprehensive review of technology innovations

“…However, this may not completely eliminate the risk of reidentification, as other types of variants or genomic features may still be informative [ 81 , 82 ]. Despite their widespread use, these k -anonymity approaches are vulnerable to attackers who have background information on the dataset [ 83 ] and are limited for high-dimensional genomic data [ 84 ].…”

“…Asymmetric or public cryptography schemes, such as Rivest–Shamir–Adleman (RSA) and elliptic curve cryptography (ECC), use 2 keys for encryption: a public key for encryption and a private key for decryption. The RSA algorithm has been used for genomic data to mask individuals’ alleles and secure cloud-based genetic paternity test results [ 83 , 112 ], but it can be slow and impractical for large Whole Genome Sequencing (WGS) files [ 112 ] and is therefore limited to smaller genomic files and sensitive metadata. ECC is often preferred over RSA for smaller genomic files due to its smaller key sizes and lower computational cost [ 113 , 114 ].…”

The Multi-Omic Approach to Newborn Screening: Opportunities and Challenges

“…Genomic data generated at a large scale, with an appropriate consent process, are theoretically reusable for healthcare and research purposes [ 98 ]. In practice, however, storing and managing genomic data and consent at scale is challenging.…”