Aftermath of bustamante attack on genomic beacon service

Abstract

BackgroundWith the enormous need for federated eco-system for holding global genomic and clinical data, Global Alliance for Genomic and Health (GA4GH) has created an international website called beacon service which allows a researcher to find out whether a specific dataset can be utilized to his or her research beforehand. This simple webservice is quite useful as it allows queries like whether a certain position of a target chromosome has a specific nucleotide. However, the increased integration of individuals genomic data into clinical practice and research raised serious privacy concern. Though the answer of such queries are yes or no in Bacon network, it results in serious privacy implication as demonstrated in a recent work from Shringarpure and Bustamante. In their attack model, the authors demonstrated that with a limited number of queries, presence of an individual in any dataset can be determined.MethodsWe propose two lightweight algorithms (based on randomized response) which captures the efficacy while preserving the privacy of the participants in a genomic beacon service. We also elaborate the strength and weakness of the attack by explaining some of their statistical and mathematical models using real world genomic database. We extend their experimental simulations for different adversarial assumptions and parameters.ResultsWe experimentally evaluated the solutions on the original attack model with different parameters for better understanding of the privacy and utility tradeoffs provided by these two methods. Also, the statistical analysis further elaborates the different aspects of the prior attack which leads to a better risk management for the participants in a beacon service.ConclusionsThe differentially private and lightweight solutions discussed here will make the attack much difficult to succeed while maintaining the fundamental motivation of beacon database network.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-017-0278-x) contains supplementary material, which is available to authorized users.

MBeacon: Privacy-Preserving Beacons for DNA Methylation Data

“…For each methylation Beacon, we randomly sample 60 patients to construct its Beacon database. We follow the approach of previous works on Beacons testing with uniform sets of patients [36], [29], [1], [45]. This ensures the attacker can only exploit individual variances and not disease-induced systematic differences, i.e., variances that are unavoidably in the data.…”

“…Despite the coarse-grained answer format, researchers have shown that the genomic Beacon is vulnerable to privacy attacks, in particular membership inference attacks [36], [29], [1], [45]. In addition, previous works have demonstrated the serious privacy risks stemming from sharing DNA methylation data [3], [8].…”

“…This example demonstrates the severe consequence of membership inference. Moreover, all the existing attacks against genomic Beacons are membership inference attacks [36], [29], [1], [45].…”

Re-Identification of Individuals in Genomic Data-Sharing Beacons via Allele Inference

“…Shringarpure and Bustamante discussed different countermeasures, such as (i) increasing the beacon size, (ii) sharing only small genomic regions, (iii) using single population beacons, (iv) not publishing the metadata of a beacon, and (v) adding control samples to the beacon dataset [13]. Lately, Aziz et al proposed two algorithms which are based on randomizing the response set of the beacons with the goal of protecting beacon members' privacy while maintaining the efficacy of the beacon servers [1]. Raisaro et al have analyzed the behavior of the beacon when applying three different countermeasures [10].…”

A Community Effort to Protect Genomic Data Sharing, Collaboration and Outsourcing

“…S3 was not chosen as a baseline in Track 2 because we assumed the beacon service does not keep track of the queries per individual. The performance of our baseline 35 and performance of the top two teams, the first from Vanderbilt University 36 and the second from the University of Manitoba, 37 are depicted in the Fig. 1.…”