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 this http URL 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.
Two recent works @cite_21 @cite_14 open a new perspective for privacy-friendly GRT by using fuzzy encryption technique. In these systems, each individual first compresses its haplotype into a 0 1 string, called private genome sketch, and then encrypts'' the sketch by using a random row of a given error correct code matrix. One may detect if user @math is relative by downloading @math 's encrypted sketch, and next decrypting'' the sketch with its own private genome sketch. If the decryption closely leads to a row in the matrix, the haplotypes of both individuals are approximately matched. However, all the aforementioned computation approaches do not really scale well in practice, as they all suffer from an important limitation, i.e., the client is burdened with heavy computation and communication overhead, as it has to download all related encrypted'' results from the server and perform a huge numbers of decryption to identify the relatedness. How to design scalable privacy-preserving GRT that can scale on both server and client side constitutes the main motivation for our work.
@cite_14 @cite_21
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Aspects of the invention include determining relatedness between genomes without compromising privacy. In one aspect, secure genome sketches of genomes can be made publicly available without compromising privacy. These are compared to privately held (unsecured) genome sketches to determine relatedness. Motivation: High-throughput sequencing technologies have impacted many areas of genetic research. One such area is the identification of relatives from genetic data. The standard approach for the identification of genetic relatives collects the genomic data of all individuals and stores it in a database. Then, each pair of individuals is compared to detect the set of genetic relatives, and the matched individuals are informed. The main drawback of this approach is the requirement of sharing your genetic data with a trusted third party to perform the relatedness test. Results: In this work, we propose a secure protocol to detect the genetic relatives from sequencing data while not exposing any information about their genomes. We assume that individuals have access to their genome sequences but do not want to share their genomes with anyone else. Unlike previous approaches, our approach uses both common and rare variants which provide the ability to detect much more distant relationships securely. We use a simulated data generated from the 1000 genomes data and illustrate that we can easily detect up to fifth degree cousins which was not possible using the existing methods. We also show in the 1000 genomes data with cryptic relationships that our method can detect these individuals. Availability: The software is freely available for download at http: genetics.cs.ucla.edu crypto . Contact: ude.alcu.sc@zomrohf or ude.alcu.sc@niksee Supplementary information: Supplementary data are available at Bioinformatics online