Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Attacks on genetic privacy via uploads to genealogical databases.

ABSTRACT: Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites across up to 82% of the genome of a median person of European ancestries. In databases that detect IBS segments using unphased genotypes, approximately 100 falsified uploads can reveal enough genetic information to allow genome-wide genetic imputation. We provide a proof-of-concept demonstration in the GEDmatch database, and we suggest countermeasures that will prevent the exploits we describe.

SUBMITTER: Edge MD

PROVIDER: S-EPMC6992384 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Attacks on genetic privacy via uploads to genealogical databases.

Edge Michael D MD Coop Graham G

eLife 20200107

Direct-to-consumer (DTC) genetics services are increasingly popular, with tens of millions of customers. Several DTC genealogy services allow users to upload genetic data to search for relatives, identified as people with genomes that share identical by state (IBS) regions. Here, we describe methods by which an adversary can learn database genotypes by uploading multiple datasets. For example, an adversary who uploads approximately 900 genomes could recover at least one allele at SNP sites acros ...[more]

PMID: 31908268

Similar Datasets

Privacy-preserving search for chemical compound databases.

Project description:BackgroundSearching for similar compounds in a database is the most important process for in-silico drug screening. Since a query compound is an important starting point for the new drug, a query holder, who is afraid of the query being monitored by the database server, usually downloads all the records in the database and uses them in a closed network. However, a serious dilemma arises when the database holder also wants to output no information except for the search results, and such a dilemma prevents the use of many important data resources.ResultsIn order to overcome this dilemma, we developed a novel cryptographic protocol that enables database searching while keeping both the query holder's privacy and database holder's privacy. Generally, the application of cryptographic techniques to practical problems is difficult because versatile techniques are computationally expensive while computationally inexpensive techniques can perform only trivial computation tasks. In this study, our protocol is successfully built only from an additive-homomorphic cryptosystem, which allows only addition performed on encrypted values but is computationally efficient compared with versatile techniques such as general purpose multi-party computation. In an experiment searching ChEMBL, which consists of more than 1,200,000 compounds, the proposed method was 36,900 times faster in CPU time and 12,000 times as efficient in communication size compared with general purpose multi-party computation.ConclusionWe proposed a novel privacy-preserving protocol for searching chemical compound databases. The proposed method, easily scaling for large-scale databases, may help to accelerate drug discovery research by making full use of unused but valuable data that includes sensitive information.

| S-EPMC4704467 | biostudies-literature

PRESAGE: PRivacy-preserving gEnetic testing via SoftwAre Guard Extension.

Project description:Advances in DNA sequencing technologies have prompted a wide range of genomic applications to improve healthcare and facilitate biomedical research. However, privacy and security concerns have emerged as a challenge for utilizing cloud computing to handle sensitive genomic data.We present one of the first implementations of Software Guard Extension (SGX) based securely outsourced genetic testing framework, which leverages multiple cryptographic protocols and minimal perfect hash scheme to enable efficient and secure data storage and computation outsourcing.We compared the performance of the proposed PRESAGE framework with the state-of-the-art homomorphic encryption scheme, as well as the plaintext implementation. The experimental results demonstrated significant performance over the homomorphic encryption methods and a small computational overhead in comparison to plaintext implementation.The proposed PRESAGE provides an alternative solution for secure and efficient genomic data outsourcing in an untrusted cloud by using a hybrid framework that combines secure hardware and multiple crypto protocols.

| S-EPMC5547453 | biostudies-other

Addressing Beacon re-identification attacks: quantification and mitigation of privacy risks.

Project description:The Global Alliance for Genomics and Health (GA4GH) created the Beacon Project as a means of testing the willingness of data holders to share genetic data in the simplest technical context-a query for the presence of a specified nucleotide at a given position within a chromosome. Each participating site (or "beacon") is responsible for assuring that genomic data are exposed through the Beacon service only with the permission of the individual to whom the data pertains and in accordance with the GA4GH policy and standards.While recognizing the inference risks associated with large-scale data aggregation, and the fact that some beacons contain sensitive phenotypic associations that increase privacy risk, the GA4GH adjudged the risk of re-identification based on the binary yes/no allele-presence query responses as acceptable. However, recent work demonstrated that, given a beacon with specific characteristics (including relatively small sample size and an adversary who possesses an individual's whole genome sequence), the individual's membership in a beacon can be inferred through repeated queries for variants present in the individual's genome.In this paper, we propose three practical strategies for reducing re-identification risks in beacons. The first two strategies manipulate the beacon such that the presence of rare alleles is obscured; the third strategy budgets the number of accesses per user for each individual genome. Using a beacon containing data from the 1000 Genomes Project, we demonstrate that the proposed strategies can effectively reduce re-identification risk in beacon-like datasets.

| S-EPMC5881894 | biostudies-literature

Privacy-preserving k-NN interpolation over two encrypted databases

Project description:Cloud computing enables users to outsource their databases and the computing functionalities to a cloud service provider to avoid the cost of maintaining a private storage and computational requirements. It also provides universal access to data, applications, and services without location dependency. While cloud computing provides many benefits, it possesses a number of security and privacy concerns. Outsourcing data to a cloud service provider in encrypted form may help to overcome these concerns. However, dealing with the encrypted data makes it difficult for the cloud service providers to perform some operations over the data that will especially be required in query processing tasks. Among the techniques employed in query processing task, the k-nearest neighbor method draws attention due to its simplicity and efficiency, particularly on massive data sets. A number of k-nearest neighbor algorithms for query processing task on a single encrypted database have been proposed. However, the performance of k-nearest neighbor algorithms on a single database may create accuracy and reliability problems. It is a fact that collaboration among different cloud service providers yields more accurate and more reliable results in query processing. By considering this fact, we focus on the k-nearest neighbor (k-NN) problem over two encrypted databases. We introduce a secure two-party k-NN interpolation protocol that enables a query owner to extract the interpolation of the k-nearest neighbors of a query point from two different databases outsourced to two different cloud service providers. We also show that our protocol protects the confidentiality of the data and the query point, and hides data access patterns. Furthermore, we conducted a number of experiment to demonstrate the efficiency of our protocol. The results show that the running time of our protocol is linearly dependent on both the number of nearest neighbours and data size.

| S-EPMC9202633 | biostudies-literature

On the effectiveness of graph matching attacks against privacy-preserving record linkage.

Project description:Linking several databases containing information on the same person is an essential step of many data workflows. Due to the potential sensitivity of the data, the identity of the persons should be kept private. Privacy-Preserving Record-Linkage (PPRL) techniques have been developed to link persons despite errors in the identifiers used to link the databases without violating their privacy. The basic approach is to use encoded quasi-identifiers instead of plain quasi-identifiers for making the linkage decision. Ideally, the encoded quasi-identifiers should prevent re-identification but still allow for a good linkage quality. While several PPRL techniques have been proposed so far, Bloom filter-based PPRL schemes (BF-PPRL) are among the most popular due to their scalability. However, a recently proposed attack on BF-PPRL based on graph similarities seems to allow individuals' re-identification from encoded quasi-identifiers. Therefore, the graph matching attack is widely considered a serious threat to many PPRL-approaches and leads to the situation that BF-PPRL schemes are rejected as being insecure. In this work, we argue that this view is not fully justified. We show by experiments that the success of graph matching attacks requires a high overlap between encoded and plain records used for the attack. As soon as this condition is not fulfilled, the success rate sharply decreases and renders the attacks hardly effective. This necessary condition does severely limit the applicability of these attacks in practice and also allows for simple but effective countermeasures.

| S-EPMC9499274 | biostudies-literature

Privacy-preserving record linkage in large databases using secure multiparty computation.

Project description:BACKGROUND:Practical applications for data analysis may require combining multiple databases belonging to different owners, such as health centers. The analysis should be performed without violating privacy of neither the centers themselves, nor the patients whose records these centers store. To avoid biased analysis results, it may be important to remove duplicate records among the centers, so that each patient's data would be taken into account only once. This task is very closely related to privacy-preserving record linkage. METHODS:This paper presents a solution to privacy-preserving deduplication among records of several databases using secure multiparty computation. It is build upon one of the fastest practical secure multiparty computation platforms, called Sharemind. RESULTS:The tests on ca 10 million records of simulated databases with 1000 health centers of 10000 records each show that the computation is feasible in practice. The expected running time of the experiment is ca. 30 min for computing servers connected over 100 Mbit/s WAN, the expected error of the results is 2-40, and no errors have been detected for the particular test set that we used for our benchmarks. CONCLUSIONS:The solution is ready for practical use. It has well-defined security properties, implied by the properties of Sharemind platform. The solution assumes that exact matching of records is required, and a possible future research would be extending it to approximate matching.

| S-EPMC6180364 | biostudies-other

Genetic data sharing and privacy.

Project description: Not available

| S-EPMC5718357 | biostudies-literature

The genetic privacy of presidential candidates.

Project description: Not available

| S-EPMC2925179 | biostudies-literature

Comparing population structure as inferred from genealogical versus genetic information.

Project description:Algorithms for inferring population structure from genetic data (ie, population assignment methods) have shown to effectively recognize genetic clusters in human populations. However, their performance in identifying groups of genealogically related individuals, especially in scanty-differentiated populations, has not been tested empirically thus far. For this study, we had access to both genealogical and genetic data from two closely related, isolated villages in southern Italy. We found that nearly all living individuals were included in a single pedigree, with multiple inbreeding loops. Despite F(st) between villages being a low 0.008, genetic clustering analysis identified two clusters roughly corresponding to the two villages. Average kinship between individuals (estimated from genealogies) increased at increasing values of group membership (estimated from the genetic data), showing that the observed genetic clusters represent individuals who are more closely related to each other than to random members of the population. Further, average kinship within clusters and F(st) between clusters increases with increasingly stringent membership threshold requirements. We conclude that a limited number of genetic markers is sufficient to detect structuring, and that the results of genetic analyses faithfully mirror the structuring inferred from detailed analyses of population genealogies, even when F(st) values are low, as in the case of the two villages. We then estimate the impact of observed levels of population structure on association studies using simulated data.

| S-EPMC2987018 | biostudies-literature

Privacy-preserving data sharing via probabilistic modeling.

Project description:Differential privacy allows quantifying privacy loss resulting from accession of sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation but would leave open the problem of designing what kind of synthetic data. We propose formulating the problem of private data release through probabilistic modeling. This approach transforms the problem of designing the synthetic data into choosing a model for the data, allowing also the inclusion of prior knowledge, which improves the quality of the synthetic data. We demonstrate empirically, in an epidemiological study, that statistical discoveries can be reliably reproduced from the synthetic data. We expect the method to have broad use in creating high-quality anonymized data twins of key datasets for research.

| S-EPMC8276015 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data