Private and Efficient Query Processing on Outsourced Genomic Databases

Ghasemi, Reza; Aziz, Momin Al; Mohammed, Noman; Dehkordi, Massoud Hadian; Jiang, Xiaoqian

doi:10.1109/jbhi.2016.2625299

Cited by 24 publications

(13 citation statements)

References 18 publications

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“…Similar problem of secure outsourcing and count query execution has been proposed by Ghasemi et al [31], Canim et al [32] and Kantarcioglu et al [33] in 2016, 2012 and 2008 respectively. In most of these works, data were kept encrypted though un-indexed and tested in smaller datasets.…”

Section: Related Workmentioning

confidence: 85%

Secure large-scale genome data storage and query

Chen

Aziz

Mohammed

et al. 2018

Computer Methods and Programs in Biomedicine

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Background and Objective Cloud computing plays a vital role in big data science with its scalable and cost-efficient architecture. Largescale genome data storage and computations would benefit from using these latest cloud computing infrastructures, to save cost and speedup discoveries. However, due to the privacy and security concerns, data owners are often disinclined to put sensitive data in a public cloud environment without enforcing some protective measures. An ideal solution is to develop secure genome database that supports encrypted data deposition and query. Methods Nevertheless, it is a challenging task to make such a system fast and scalable enough to handle real-world demands providing data security as well. In this paper, we propose a novel, secure mechanism to support secure count queries on an open source graph database (Neo4j) and evaluated the performance on a realworld dataset of around 735,317 Single Nucleotide Polymorphisms (SNPs). In particular, we propose a new tree indexing method that offers constant time complexity (proportion to the tree depth), which was the bottleneck of existing approaches. Results The proposed method significantly improves the runtime of query execution compared to the existing techniques. It takes less than one minute to execute an arbitrary count query on a dataset of 212 GB, while the best-known algorithm takes around 7 minutes. Conclusions The outlined framework and experimental results show the applicability of utilizing graph database for securely storing large-scale genome data in untrusted environment. Furthermore, the crypto-system and security assumptions underlined are much suitable for such use cases which be generalized in future work.

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Section: Related Workmentioning

confidence: 85%

Secure large-scale genome data storage and query

Chen

Aziz

Mohammed

et al. 2018

Computer Methods and Programs in Biomedicine

Self Cite

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“…The computational complexity of PrivGenDB in this phase is in the order of rx. All methods in Table III are linear in rx, except [20] which does not encrypt all the data and [17] which depends on the number of nodes (increases by x and r).…”

Section: A Initialisation Computational Costmentioning

confidence: 99%

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

Jafarbeiki¹,

Sakzad²,

Kermanshahi³

et al. 2021

Preprint

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In this article, we propose a novel model, Priv-GenDB, for securely storing and efficiently conducting different queries on genomic data outsourced to an honest-but-curious cloud server. To instantiate PrivGenDB, we use searchable symmetric encryption (SSE) to ensure confidentiality while providing the required functionality. To the best of our knowledge, Priv-GenDB construction is the first SSE-based approach ensuring the confidentiality of shared single nucleotide polymorphism (SNP)-phenotype data through encryption while making the computation/query process efficient and scalable for biomedical research and care. It supports a variety of query types on genomic data, including count queries, Boolean queries, and k -out-of-k match queries. Finally, the PrivGenDB model not only can handle the dataset containing both genotype and phenotype, but it also supports storing and managing other metadata like gender and ethnicity privately. Computer evaluations on a dataset with 5, 000 records and 1, 000 SNPs demonstrate that a count/Boolean query and a k -out-of-k match query over 40 SNPs take approximately 4.3s and 86.4µs, respectively, that outperforms the existing schemes.

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“…Securing shared or externalized genetic association studies does not simply mean securing the storage and transmission of genomic data [25], [26]. Indeed, parties involved in such studies may not want that the other parties access their data, the objective and the conclusions of the study, these ones being highly valuable assets.…”

Section: A Related Workmentioning

confidence: 99%

Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

Bellafqira¹,

Ludwig²,

Niyitegeka³

et al. 2020

IEEE Access

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This paper proposes a new privacy-preserving framework to perform rare variant casecontrol association tests with information provided by two parties: a Genomic Research Unit (GRU) with sequencing data from individuals affected by a disease D (cases); a Genomic Research Center (GRC) with sequencing data from healthy individuals (controls). To identify genes with rare variants involved in D, GRU needs to compare cases against controls using association tests (genome-wide association study). The main originality of our proposal is twofold. First, it positions GRC as a proxy between GRU and the server. Doing so makes it possible to use classical cryptographic tools to securely conduct association tests with no computation complexity increase, contrarily to actual state of the art proposals which are of very high complexity being based on homomorphic encryption, for instance. In particular, we show how sensitive data confidentiality can be ensured with secret key based cryptographic hashing with no need to modify statistical algorithms. In our protocol the server simply conducts statistical analyses on partially hashed data. Secondly, we introduce a novel privacy constraint: GRU's identity should remain unknown to the server as this knowledge can give it clues about GRU's data (e.g., diseases and genes of interest). We exhibit how Pretty Good Privacy (PGP) can be used to solve this problem. We illustrate our protocol in the case of one rare variant association test, the Weighted-Sum Statistic (WSS) algorithm, carried out on real genetic data. This secure WSS achieves the same accuracy as its nonsecure version with no increase of complexity. Furthermore, we establish that our protocol can be extended to the different rare variant association tests available in the literature.

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Private and Efficient Query Processing on Outsourced Genomic Databases

Cited by 24 publications

References 18 publications

Secure large-scale genome data storage and query

Secure large-scale genome data storage and query

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

Privacy-Preserving Genome-Wide Association Study for Rare Mutations - A Secure FrameWork for Externalized Statistical Analysis

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