29 research outputs found
Privacy-Preserving Genetic Relatedness Test
An increasing number of individuals are turning to Direct-To-Consumer (DTC)
genetic testing to learn about their predisposition to diseases, traits, and/or
ancestry. DTC companies like 23andme and Ancestry.com have started to offer
popular and affordable ancestry and genealogy tests, with services allowing
users to find unknown relatives and long-distant cousins. Naturally, access and
possible dissemination of genetic data prompts serious privacy concerns, thus
motivating the need to design efficient primitives supporting private genetic
tests. In this paper, we present an effective protocol for privacy-preserving
genetic relatedness test (PPGRT), enabling a cloud server to run relatedness
tests on input an encrypted genetic database and a test facility's encrypted
genetic sample. We reduce the test to a data matching problem and perform it,
privately, using searchable encryption. Finally, a performance evaluation of
hamming distance based PP-GRT attests to the practicality of our proposals.Comment: A preliminary version of this paper appears in the Proceedings of the
3rd International Workshop on Genome Privacy and Security (GenoPri'16
Privacy and security in the genomic era
With the help of rapidly developing technology, DNA sequencing is becoming less expensive. As a consequence, the research in genomics has gained speed in paving the way to personalized (genomic) medicine, and geneticists need large collections of human genomes to further increase this speed. Furthermore, individuals are using their genomes to learn about their (genetic) predispositions to diseases, their ancestries, and even their (genetic) compatibilities with potential partners. This trend has also caused the launch of health-related websites and online social networks (OSNs), in which individuals share their genomic data (e.g., Open-SNP or 23 and Me). On the other hand, genomic data carries much sensitive information about its owner. By analyzing the DNA of an individual, it is now possible to learn about his disease predispositions (e.g., for Alzheimer's or Parkinson's), ancestries, and physical attributes. The threat to genomic privacy is magnified by the fact that a person's genome is correlated to his family members' genomes, thus leading to interdependent privacy risks. This short tutorial will help computer scientists better understand the privacy and security challenges in today's genomic era. We will first highlight the significance of genomic data and the threats for genomic privacy. Then, we will present the high level descriptions of the proposed solutions to protect the privacy of genomic data and we will discuss future research directions. No prerequisite knowledge on biology or genomics is required for the attendees of this proposal. We only require the attendees to have a slight background on cryptography and statistics. © 2016 Copyright held by the owner/author(s)
Fast and Private Genomic Testing for Disease Susceptibility
Advances in DNA sequencing are bringing mass computational genomic testing increasingly closer to reality. The sensitivity of genetic data, however, prompts the need for carefully protecting patients' privacy. Also, it is crucial to conceal the test's specifics, which often constitute a pharmaceutical company's trade secret. This paper presents two cryptographic protocols for privately assessing a patient's genetic susceptibility to a disease, computing a weighted average of patient's genetic markers (the ``SNPs'') and their importance factor. We build on the architecture introduced by Ayday et al. but point out an important limitation of their model, namely, that the protocol leaks which and how many SNPs are tested. Then, we demonstrate that an alternative SNP encoding can simplify (private) computations, and make patient-side computation on a smartcard device extremely efficient. A second protocol variant, based on secret sharing, further reduces online computation
Constructive Privacy for Shared Genetic Data
International audienceThe 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
Privacy in the Genomic Era
Genome sequencing technology has advanced at a rapid pace and it is now
possible to generate highly-detailed genotypes inexpensively. The collection
and analysis of such data has the potential to support various applications,
including personalized medical services. While the benefits of the genomics
revolution are trumpeted by the biomedical community, the increased
availability of such data has major implications for personal privacy; notably
because the genome has certain essential features, which include (but are not
limited to) (i) an association with traits and certain diseases, (ii)
identification capability (e.g., forensics), and (iii) revelation of family
relationships. Moreover, direct-to-consumer DNA testing increases the
likelihood that genome data will be made available in less regulated
environments, such as the Internet and for-profit companies. The problem of
genome data privacy thus resides at the crossroads of computer science,
medicine, and public policy. While the computer scientists have addressed data
privacy for various data types, there has been less attention dedicated to
genomic data. Thus, the goal of this paper is to provide a systematization of
knowledge for the computer science community. In doing so, we address some of
the (sometimes erroneous) beliefs of this field and we report on a survey we
conducted about genome data privacy with biomedical specialists. Then, after
characterizing the genome privacy problem, we review the state-of-the-art
regarding privacy attacks on genomic data and strategies for mitigating such
attacks, as well as contextualizing these attacks from the perspective of
medicine and public policy. This paper concludes with an enumeration of the
challenges for genome data privacy and presents a framework to systematize the
analysis of threats and the design of countermeasures as the field moves
forward