PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Jafarbeiki, Sara; Sakzad, Amin; Kermanshahi, Shabnam Kasra; Gaire, Raj; Steinfeld, Ron; Lai, Shangqi; Abraham, Gad

doi:10.1016/j.imu.2022.100988

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Threat Model. Gyosa adopts the standard SGX threat model supported by existing research [20,9,17]. We consider a scenario where a client seeks to use sensitive genomic data and perform computation on top of it at third-party infrastructures.…”

Section: Designmentioning

confidence: 99%

“…HE enables arithmetic operations (e.g., sums and aggregations) over encrypted data, thus protecting sensitive content when stored and processed. Despite its recent advances, it has been shown that for GWAS, this technique still imposes a high penalty on execution time and can only support a limited number of algorithmic operations [17]. SMPC enables secure data storage and computation even across multiple entities that do not trust each other.…”

Section: Introductionmentioning

confidence: 99%

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A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Brito,

Ferreira,

Paulo

2024

Preprint

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Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing unprecedented biological insights and new therapeutic applications. However, analyzing such large amounts of sensitive data raises key concerns regarding data privacy, specifically when the information is outsourced to third-party infrastructures for data storage and processing (e.g., cloud computing). Current solutions for data privacy protection resort to centralized designs or cryptographic primitives that impose considerable computational overheads, limiting their applicability to large-scale genomic analysis. We introduce GYOSA, a secure and privacy-preserving distributed genomic analysis solution. Unlike in previous work, GYOSA follows a distributed processing design that enables handling larger amounts of genomic data in a scalable and efficient fashion. Further, by leveraging trusted execution environments (TEEs), namely Intel SGX, GYOSA allows users to confidentially delegate their GWAS analysis to untrusted third-party infrastructures. To overcome the memory limitations of SGX, we implement a computation partitioning scheme within GYOSA. This scheme reduces the number of operations done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, GYOSA provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of GYOSA, reinforcing its ability to provide enhanced security guarantees. Further, the results show that, by distributing GWASes computations, one can achieve a practical and usable privacy-preserving solution.

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Section: Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Brito,

Ferreira,

Paulo

2024

Preprint

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Efficient and Secure Count Queries on Encrypted Genomic Data

Hu,

Liu,

Tuo

et al. 2024

Communications in Computer and Information Science

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SecCT: Secure and Scalable Count Query Models on Encrypted Genomic Data

Peng,

Liu,

Guo

et al. 2024

Form. Asp. Comput.

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Recently, due to the continued reduction in DNA sequencing cost, large-scale genetic samples are being gathered for accelerating predispositions to specific diseases, tailoring treatment of efficient drugs and therapies, etc. Massive genetic samples are encrypted-and-then-delegated to a public cloud to both save investment and maintenance costs and prevent the potential leakage of sensitive information. However, such a manner compromises the serviceability of a public cloud, since encryption inevitably breaks the semantic information of genetic samples. Secure count query of single-nucleotide polymorphisms (SNPs), as a kernel component for GWASs and related genomic analysis, is attracting much more attention. Existing methods lack provable security, suffer low efficiency caused by multiple interactions with the cloud, etc. In this paper, a secure virtual CT-Tree (secure vCT-Tree) is carefully constructed to confuse the tree structure by introducing a hash function and a Paillier system. Furthermore, by delegating the secure vCT-Tree to the cloud, concrete models (i.e., SecCT and SecCT+) are presented to resolve secure count query problems on-the-fly. SecCT+ is a solution based on trusted execution environment while SecCT is a pure software solution. Both models advance the provable security of genetic research and are proven to be secure under the adaptive chosen keyword (query) attack (IND-CKA2) model. Furthermore, massive experiments are evaluated on realistic data to show the superiority of SecCT and SecCT+.

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PrivGenDB: Efficient and privacy-preserving query executions over encrypted SNP-Phenotype database

Cited by 3 publications

References 31 publications

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

A Distributed Computing Solution for Privacy-Preserving Genome-Wide Association Studies

Efficient and Secure Count Queries on Encrypted Genomic Data

SecCT: Secure and Scalable Count Query Models on Encrypted Genomic Data

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