A new way to protect privacy in large-scale genome-wide association studies

Kamm, Liina; Bogdanov, Dan; Laur, Sven; Vilo, Jaak

doi:10.1093/bioinformatics/btt066

Cited by 100 publications

(97 citation statements)

References 37 publications

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“…Prior to our work, different authors have proposed various techniques for addressing privacy concerns in genome data processing, ranging from differential privacy, secret sharing and homomorphic encryption [10]. Specifically, Wu and Haven [11] demonstrated a secure computing of statistical analysis algorithms over encrypted data.…”

Section: Related Workmentioning

confidence: 99%

Privacy-safe linkage analysis with homomorphic encryption

Ugwuoke

Erkin

Lagendijk

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-Genetic data are important dataset utilised in genetic epidemiology to investigate biologically coded information within the human genome. Enormous research has been delved into in recent years in order to fully sequence and understand the genome. Personalised medicine, patient response to treatments and relationships between specific genes and certain characteristics such as phenotypes and diseases, are positive impacts of studying the genome, just to mention a few. The sensitivity, longevity and non-modifiable nature of genetic data make it even more interesting, consequently, the security and privacy for the storage and processing of genomic data beg for attention. A common activity carried out by geneticists is the association analysis between allele-allele, or even a genetic locus and a disease. We demonstrate the use of cryptographic techniques such as homomorphic encryption schemes and multiparty computations, how such analysis can be carried out in a privacy friendly manner. We compute a 3 × 3 contingency table, and then, genome analyses algorithms such as linkage disequilibrium (LD) measures, all on the encrypted domain. Our computation guarantees privacy of the genome data under our security settings, and provides up to 98.4% improvement, compared to an existing solution.

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Section: Related Workmentioning

confidence: 99%

Privacy-safe linkage analysis with homomorphic encryption

Ugwuoke

Erkin

Lagendijk

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…Our contribution. This paper builds on our earlier work in [5,37] where we presented our first attempts on privacy-preserving statistics. In this paper, we have made major improvements.…”

Section: Introductionmentioning

confidence: 99%

Rmind: A Tool for Cryptographically Secure Statistical Analysis

Bogdanov

Kamm

Laur

et al. 2018

IEEE Trans. Dependable and Secure Comput.

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Abstract. Secure multi-party computation platforms are becoming more and more practical. This has paved the way for privacy-preserving statistical analysis using secure multi-party computation. Simple statistical analysis functions have been emerging here and there in literature, but no comprehensive system has been compiled. We describe and implement the most used statistical analysis functions in the privacy-preserving setting including simple statistics, t-test, χ 2 test, Wilcoxon tests and linear regression. We give descriptions of the privacy-preserving algorithms and benchmark results that show the feasibility of our solution.

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“…Data is shared using a client/server architecture where the server is often a cloud provider storing and processing, e.g., homomorphically encrypted data (Lu et al, 2015;Zhang et al, 2015), which enables computations to be directly performed on encrypted data; another architecture consists in the collaboration between different biomedical sites which make use of multi-party computation protocols. However, the corresponding approaches, e.g., (Liina Kamm et al, 2013;Tang et al, 2016), only handle a limited number of sites in the scenario and are yet to be extended so to handle realistic scenarios for a wider sharing of genetic data.…”

Section: Introductionmentioning

confidence: 99%

Constructive Privacy for Shared Genetic Data

Boujdad

Südholt

2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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Abstract:The need for the sharing of genetic data, for instance, in genome-wide association studies is incessantly growing. In parallel, serious privacy concerns rise from a multi-party access to genetic information. Several techniques, such as encryption, have been proposed as solutions for the privacy-preserving sharing of genomes. However, existing programming means do not support guarantees for privacy properties and the performance optimization of genetic applications involving shared data. We propose two contributions in this context. First, we present new cloud-based architectures for cloud-based genetic applications that are motivated by the needs of geneticians. Second, we propose a model and implementation for the composition of watermarking with encryption, fragmentation, and client-side computations for the secure and privacy-preserving sharing of genetic data in the cloud.

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A new way to protect privacy in large-scale genome-wide association studies

Cited by 100 publications

References 37 publications

Privacy-safe linkage analysis with homomorphic encryption

Privacy-safe linkage analysis with homomorphic encryption

Rmind: A Tool for Cryptographically Secure Statistical Analysis

Constructive Privacy for Shared Genetic Data

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