Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Mittos, Alexandros; Malin, Bradley; Cristofaro, Emiliano De

doi:10.2478/popets-2019-0006

Cited by 35 publications

(23 citation statements)

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“…Early studies at the intersection of these domains focused on privacy-respecting methods for processing genetic data [4], [24], [36]. The field has continued to expand, as captured by surveys such as Akgün, et al [2], Mittos, Malin, and De Cristofaro [30], and Naveed, et al [31]. The surveyed privacy concerns and associated defenses range from privacy risks with genetic testing services, to data storage, to the computation over genetic data by untrusted parties.…”

Section: Genome Privacymentioning

confidence: 99%

Genotype Extraction and False Relative Attacks: Security Risks to Third-Party Genetic Genealogy Services Beyond Identity Inference

Ney

Ceze

Kohno

2020

Proceedings 2020 Network and Distributed System Security Symposium

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Customers of direct-to-consumer (DTC) genetic testing services routinely download their raw genetic data and give it to third-party companies that support additional features. One type of analysis, called genetic genealogy, uses genetic data and genealogical methods to find new relatives. While genetic genealogy is quite popular, it has raised new privacy concerns. Genetic genealogy services can be leveraged to find the person corresponding to anonymous genetic data and have been used dozens of times by law enforcement to solve crimes. We hypothesized that the open design and broad API offered by some genetic genealogy services raise other significant security and privacy issues. To test this hypothesis, we analyzed the security practices of GEDmatch, the largest third-party genetic genealogy service. Here, we experimentally show how the GEDmatch API is vulnerable to a number of attacks from an adversary that only uploads normally formatted genetic data files and runs standard queries. Using a small number of specifically designed files and queries, an attacker can extract a large percentage of the genetic markers from other users; 92% of markers can be extracted with 98% accuracy, including hundreds of medically sensitive markers. We also find that an adversary can construct genetic data files that falsely appear like relatives to other samples in the database; in certain situations, these false relatives can be used to make the re-identification of genetic data more difficult. These attacks are possible because of the rich set of features supported by the API, including detailed visualizations, that are meant to enhance usability. We conclude with security recommendations for genetic genealogy services.

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Section: Genome Privacymentioning

confidence: 99%

Genotype Extraction and False Relative Attacks: Security Risks to Third-Party Genetic Genealogy Services Beyond Identity Inference

Ney

Ceze

Kohno

2020

Proceedings 2020 Network and Distributed System Security Symposium

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“…For this reason, we compare whenever is possible the secure versions to the nonsecure versions of the same functionality. Inspired by [57], we choose different criteria aiming at capturing different aspects related to security, efficiency, and data utility. They correspond to:…”

Section: Comparison To the Existing Solutionsmentioning

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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“…The privacy of other applications of genomics were also addressed in the literature, e.g., outsourcing of genome data storing [41], pattern matching [43], genome sequence queries [17], and Genome-Wide Association Studies (GWAS) [10,11,15,42], see [33] for a good survey on genomic privacy.…”

Section: Related Workmentioning

confidence: 99%

EPISODE: Efficient Privacy-PreservIng Similar Sequence Queries on Outsourced Genomic DatabasEs

Schneider

Tkachenko

2019

Proceedings of the 2019 ACM Asia Conference on Computer and Communications Security

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Nowadays, genomic sequencing has become much more affordable for many people and, thus, many people own their genomic data in a digital format. Having paid for genomic sequencing, they want to make use of their data for different tasks that are possible only using genomics, and they share their data with third parties to achieve these tasks, e.g., to find their relatives in a genomic database. As a consequence, more genomic data get collected worldwide. The upside of the data collection is that unique analyses on these data become possible. However, this raises privacy concerns because the genomic data uniquely identify their owner, contain sensitive data about his/her risk for getting particular diseases, and even sensitive information about his/her family members. In this paper, we introduce EPISODE-a highly efficient privacypreserving protocol for Similar Sequence Queries (SSQs), which can be used for finding genetically similar individuals in an outsourced genomic database, i.e., securely aggregated from data of multiple institutions. Our SSQ protocol is based on the edit distance approximation by Asharov et al. (PETS'18), which we further optimize and extend to the outsourcing scenario. We improve their protocol by using more efficient building blocks and achieve a 5-6× runtime improvement compared to their work in the same two-party scenario. Recently, Cheng et al. (ASIACCS'18) introduced protocols for outsourced SSQs that rely on homomorphic encryption. Our new protocol outperforms theirs by more than factor 24 000× in terms of run-time in the same setting and guarantees the same level of security. In addition, we show that our algorithm scales for practical database sizes by querying a database that contains up to a million short sequences within a few minutes, and a database with hundreds of whole-genome sequences containing 75 million alleles each within a few hours. CCS CONCEPTS • Security and privacy → Privacy-preserving protocols; Management and querying of encrypted data; Privacy protections; * A summary of preliminary results of this paper has been published as short paper at WPES'18 [38]. This is the full version.

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Systematizing Genome Privacy Research: A Privacy-Enhancing Technologies Perspective

Cited by 35 publications

References 100 publications

Genotype Extraction and False Relative Attacks: Security Risks to Third-Party Genetic Genealogy Services Beyond Identity Inference

Genotype Extraction and False Relative Attacks: Security Risks to Third-Party Genetic Genealogy Services Beyond Identity Inference

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

EPISODE: Efficient Privacy-PreservIng Similar Sequence Queries on Outsourced Genomic DatabasEs

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