Biology subjects

Ayday, E.

Publications and source records attributed to Ayday, E..

5 recordsLinked to original sources

bioRxiv2024

A Reinforcement Learning-based Approach for Dynamic Privacy Protection in Genomic Data Sharing Beacons

The rise of genomic sequencing has led to significant privacy concerns due to the sensitive and identifiable nature of genomic data. The Beacon Project, initiated by the Global Alliance for Genomics and Health (GA4GH), was designed to enable privacy-preserving sharing of genomic information via an online querying system. However, studies have revealed that the protocol is vulnerable to membership inference attacks, which can expose the presence of individuals in sensitive datasets. Various countermeasures, such as noise addition and query restrictions, have been proposed but are limited by static implementation, leaving them prone to attackers that can adapt and change strategies. In this study, we present the first reinforcement learning (RL)-based approach for dynamic privacy protection of the beacon protocol. We employ a multi-player RL setting where we train (i) a "Generic-Beacon-Defender" agent who can adjust the honesty rate of its responses, against (ii) a "Generic-Beacon-Attacker" agent who can choose the order of the queries and ask random queries to make the beacon think it is a regular user. This is the first defense mechanism capable of adapting its strategy in real time based on user queries, distinguishing between legitimate users and potential attackers, and applying tailored policies accordingly. By doing so, this method enhances both privacy and utility, effectively countering sophisticated and evolving threats. The code and the models are available at github.com/ciceklab/beacon-defense-strategies.

bioinformatics↗

bioRxiv2024

Safeguarding Privacy in Genome Research: A Comprehensive Framework for Authors

As genomic research continues to advance, sharing of genomic data and research outcomes has become increasingly important for fostering collaboration and accelerating scientific discovery. However, such data sharing must be balanced with the need to protect the privacy of individuals whose genetic information is being utilized. This paper presents a bidirectional framework for evaluating privacy risks associated with data shared (both in terms of summary statistics and research datasets) in genomic research papers, particularly focusing on re-identification risks such as membership inference attacks (MIA). The framework consists of a structured workflow that begins with a questionnaire designed to capture researchers (authors) self-reported data sharing practices and privacy protection measures. Responses are used to calculate the risk of re-identification for their study (paper) when compared with the National Institutes of Health (NIH) genomic data sharing policy. Any gaps in compliance help us to identify potential vulnerabilities and encourage the researchers to enhance their privacy measures before submitting their research for publication. The paper also demonstrates the application of this framework, using published genomic research as case study scenarios to emphasize the importance of implementing bidirectional frameworks to support trustworthy open science and genomic data sharing practices.

genomics↗

bioRxiv2020

Privacy-Preserving and Robust Watermarking on Sequential Genome Data using Belief Propagation and Local Differential Privacy

MotivationGenome data is a subject of study for both biology and computer science since the start of Human Genome Project in 1990. Since then, genome sequencing for medical and social purposes becomes more and more available and affordable. Genome data can be shared on public websites or with service providers. However, this sharing compromises the privacy of donors even under partial sharing conditions. We mainly focus on the liability aspect ensued by unauthorized sharing of these genome data. One of the techniques to address the liability issues in data sharing is watermarking mechanism. ResultsTo detect malicious correspondents and service providers (SPs) -whose aim is to share genome data without individuals consent and undetected-, we propose a novel watermarking method on sequential genome data using belief propagation algorithm. In our method, we have two criteria to satisfy. (i) Embedding robust watermarks so that the malicious adversaries can not temper the watermark by modification and are identified with high probability (ii) Achieving{epsilon} -local differential privacy in all data sharings with SPs. For the preservation of system robustness against single SP and collusion attacks, we consider publicly available genomic information like Minor Allele Frequency, Linkage Disequilibrium, Phenotype Information and Familial Information. Our proposed scheme achieves 100% detection rate against the single SP attacks with only 3% watermark length. For the worst case scenario of collusion attacks (50% of SPs are malicious), 80% detection is achieved with 5% watermark length and 90% detection is achieved with 10% watermark length. For all cases,{epsilon} s impact on precision remained negligible and high privacy is ensured. Availabilityhttps://github.com/acoksuz/PPRW_SGD_BPLDP Contactabdullahcaglaroksuz@gmail.com

bioinformatics↗

bioRxiv2020

The Effect of Kinship in Re-identification Attacks Against Genomic Data Sharing Beacons

Motivation Big data era in genomics promises a breakthrough in medicine, but sharing data in a private manner limits the pace of field. Widely accepted “genomic data-sharing beacon” protocol provides a standardized and secure interface for querying the genomic datasets. The data is only shared if the desired information (e.g., a certain variant) exists in the dataset. Various studies showed that beacons are vulnerable to re-identification (or membership inference) attacks. As beacons are generally associated with sensitive phenotype information, re-identification creates a significant risk for the participants. Unfortunately, proposed countermeasures against such attacks have failed to be effective, as they do not consider the utility of beacon protocol.Results In this study, for the first time, we analyze the mitigation effect of the kinship relationships among beacon participants against re-identification attacks. We argue that having multiple family members in a beacon can garble the information for attacks since a substantial number of variants are shared among kinrelated people. Using family genomes from HapMap and synthetically-generated datasets, we show that having one of the parents of a victim in the beacon causes (i) significant decrease in the power of attacks and (ii) substantial increase in the number of queries needed to confirm an individual’s beacon membership. We also show how the protection effect attenuates when more distant relatives, such as grandparents are included alongside the victim. Furthermore, we quantify the utility loss due adding relatives and show that it is smaller compared to flipping based techniques.Competing Interest StatementThe authors have declared no competing interest.View Full Text

bioinformatics↗

bioRxiv2017

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

Genomic datasets are often associated with sensitive phenotypes. Therefore, the leak of membership information is a major privacy risk. Genomic beacons aim to provide a secure, easy to implement, and standardized interface for data sharing by only allowing yes/no queries on the presence of specific alleles in the dataset. Previously deemed secure against re-identification attacks, beacons were shown to be vulnerable despite their stringent policy. Recent studies have demonstrated that it is possible to determine whether the victim is in the dataset, by repeatedly querying the beacon for his/her single nucleotide polymorphisms (SNPs). In this work, we propose a novel re-identification attack and show that the privacy risk is more serious than previously thought. Using the proposed attack, even if the victim systematically hides informative SNPs (i.e., SNPs with very low minor allele frequency -MAF-), it is possible to infer the alleles at positions of interest as well as the beacon query results with very high confidence. Our method is based on the fact that alleles at different loci are not necessarily independent. We use the linkage disequilibrium and a high-order Markov chain-based algorithm for the inference. We show that in a simulated beacon with 65 individuals from the CEU population, we can infer membership of individuals with 95% confidence with only 5 queries, even when SNPs with MAF less than 0.05 are hidden. This means, we need less than 0.5% of the number of queries that existing works require, to determine beacon membership under the same conditions. We further show that countermeasures such as hiding certain parts of the genome or setting a query budget for the user would fail to protect the privacy of the participants under our adversary model.

bioinformatics↗