DyPS: Dynamic, Private and Secure GWAS

Abstract

Genome-Wide Association Studies (GWAS) identify the genomic variations that are statistically associated with a particular phenotype (e.g., a disease). The confidence in GWAS results increases with the number of genomes analyzed, which encourages federated computations where biocenters would periodically share the genomes they have sequenced. However, for economical and legal reasons, this collaboration will only happen if biocenters cannot learn each others’ data. In addition, GWAS releases should not jeopardize the privacy of the individuals whose genomes are used. We introduce DyPS, a novel framework to conduct dynamic privacy-preserving federated GWAS. DyPS leverages a Trusted Execution Environment to secure dynamic GWAS computations. Moreover, DyPS uses a scaling mechanism to speed up the releases of GWAS results according to the evolving number of genomes used in the study, even if individuals retract their participation consent. Lastly, DyPS also tolerates up to all-but-one colluding biocenters without privacy leaks. We implemented and extensively evaluated DyPS through several scenarios involving more than 6 million simulated genomes and up to 35,000 real genomes. Our evaluation shows that DyPS updates test statistics with a reasonable additional request processing delay (11% longer) compared to an approach that would update them with minimal delay but would lead to 8% of the genomes not being protected. In addition, DyPS can result in the same amount of aggregate statistics as a static release (i.e., at the end of the study), but can produce up to 2.6 times more statistics information during earlier dynamic releases. Besides, we show that DyPS can support a larger number of genomes and SNP positions without any significant performance penalty.

The evolving privacy and security concerns for genomic data analysis and sharing as observed from the iDASH competition

“…Recent studies show that the hybrid approaches that combine hardware (eg, SGX) and software (eg, HE and secure multiparty computation) offer efficient solutions to genomic data analyses. For example, SAFETY 75 and DyPS 76 are hybrid computational frameworks to perform secure GWAS on distributed genomic datasets using HE and SGX techniques. Kockan et al 77 developed an approximation algorithm to accelerate a secure GWAS algorithm running in SGX that achieves comparable accuracy and efficiency to those of nonsecure counterparts.…”

Digital DNA lifecycle security and privacy: an overview

“…Wu et al [76] introduced a privacy-preserving framework for federated GWAS where genomic data is computed locally within each participating institute, and only aggregated local statistics are exchanged within the study network. Pascoal et al [88] introduced Dynamic, Private and Secure (DyPS) GWAS which is a federated GWAS system where each biocentre shares its statistics without revealing its data. All statistics are computed securely within Intel SGX while preserving privacy by safely releasing aggregated statistics after passing several privacy checks i.e.…”

Privacy-Utility Equilibrium Protocol for Federated Aggregating Multiparty Genome Data

“…But the major problem of aggregating genome data cross-institution is privacy concerns. Thus, existing work uses cryptography methods achieving privacy preserving of federated genome data [2], [3], [28], [29], such as secure multiparty computation, homomorphic encryption, and Intel SGX. Blockchain-based time-stamping scheme [30] can also be used to guarantee privacy of federated genome data in cloud storage.…”