Achieving data privacy for decision support systems in times of massive data sharing.
Ontology highlight
ABSTRACT:
SUBMITTER: Fazal R
PROVIDER: S-EPMC8743442 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:BackgroundData sharing accelerates scientific progress but sharing individual-level data while preserving patient privacy presents a barrier.Methods and resultsUsing pairs of deep neural networks, we generated simulated, synthetic participants that closely resemble participants of the SPRINT trial (Systolic Blood Pressure Trial). We showed that such paired networks can be trained with differential privacy, a formal privacy framework that limits the likelihood that queries of the synthetic participants' data could identify a real a participant in the trial. Machine learning predictors built on the synthetic population generalize to the original data set. This finding suggests that the synthetic data can be shared with others, enabling them to perform hypothesis-generating analyses as though they had the original trial data.ConclusionsDeep neural networks that generate synthetic participants facilitate secondary analyses and reproducible investigation of clinical data sets by enhancing data sharing while preserving participant privacy.
| S-EPMC7041894 | biostudies-literature
Project description:Effective data sharing is critical for comparative effectiveness research (CER), but there are significant concerns about inappropriate disclosure of patient data. These concerns have spurred the development of new technologies for privacy-preserving data sharing and data mining. Our goal is to review existing and emerging techniques that may be appropriate for data sharing related to CER.We adapted a systematic review methodology to comprehensively search the research literature. We searched 7 databases and applied 3 stages of filtering based on titles, abstracts, and full text to identify those works most relevant to CER.On the basis of agreement and using the arbitrage of a third party expert, we selected 97 articles for meta-analysis. Our findings are organized along major types of data sharing in CER applications (ie, institution-to-institution, institution hosted, and public release). We made recommendations based on specific scenarios.We limited the scope of our study to methods that demonstrated practical impact, eliminating many theoretical studies of privacy that have been surveyed elsewhere. We further limited our study to data sharing for data tables, rather than complex genomic, set valued, time series, text, image, or network data.State-of-the-art privacy-preserving technologies can guide the development of practical tools that will scale up the CER studies of the future. However, many challenges remain in this fast moving field in terms of practical evaluations and applications to a wider range of data types.
| S-EPMC3728160 | biostudies-literature
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
Project description:Greater sharing of potentially sensitive data raises important ethical, legal and social issues (ELSI), which risk hindering and even preventing useful data sharing if not properly addressed. One such important issue is respecting the privacy-related interests of individuals whose data are used in genomic research and clinical care. As part of the Global Alliance for Genomics and Health (GA4GH), we examined the ELSI status of health-related data that are typically considered 'sensitive' in international policy and data protection laws. We propose that 'tiered protection' of such data could be implemented in contexts such as that of the GA4GH Beacon Project to facilitate responsible data sharing. To this end, we discuss a Data Sharing Privacy Test developed to distinguish degrees of sensitivity within categories of data recognised as 'sensitive'. Based on this, we propose guidance for determining the level of protection when sharing genomic and health-related data for the Beacon Project and in other international data sharing initiatives.
| S-EPMC5158304 | biostudies-literature
Project description:The human genetics community needs robust protocols that enable secure sharing of genomic data from participants in genetic research. Beacons are web servers that answer allele-presence queries--such as "Do you have a genome that has a specific nucleotide (e.g., A) at a specific genomic position (e.g., position 11,272 on chromosome 1)?"--with either "yes" or "no." Here, we show that individuals in a beacon are susceptible to re-identification even if the only data shared include presence or absence information about alleles in a beacon. Specifically, we propose a likelihood-ratio test of whether a given individual is present in a given genetic beacon. Our test is not dependent on allele frequencies and is the most powerful test for a specified false-positive rate. Through simulations, we showed that in a beacon with 1,000 individuals, re-identification is possible with just 5,000 queries. Relatives can also be identified in the beacon. Re-identification is possible even in the presence of sequencing errors and variant-calling differences. In a beacon constructed with 65 European individuals from the 1000 Genomes Project, we demonstrated that it is possible to detect membership in the beacon with just 250 SNPs. With just 1,000 SNP queries, we were able to detect the presence of an individual genome from the Personal Genome Project in an existing beacon. Our results show that beacons can disclose membership and implied phenotypic information about participants and do not protect privacy a priori. We discuss risk mitigation through policies and standards such as not allowing anonymous pings of genetic beacons and requiring minimum beacon sizes.
| S-EPMC4667107 | biostudies-literature
Project description:Although the privacy issues in human genomic studies are well known, the privacy risks in clinical proteomic data have not been thoroughly studied. As a proof of concept, we reported a comprehensive analysis of the privacy risks in clinical proteomic data. It showed that a small number of peptides carrying the minor alleles (referred to as the minor allelic peptides) at non-synonymous single nucleotide polymorphism (nsSNP) sites can be identified in typical clinical proteomic datasets acquired from the blood/serum samples of individual patient, from which the patient can be identified with high confidence. Our results suggested the presence of significant privacy risks in raw clinical proteomic data. However, these risks can be mitigated by a straightforward pre-processing step of the raw data that removing a very small fraction (0.1%, 7.14 out of 7,504 spectra on average) of MS/MS spectra identified as the minor allelic peptides, which has little or no impact on the subsequent analysis (and re-use) of these datasets.
| S-EPMC5009298 | biostudies-literature
Project description:The sharing of genomic data holds great promise in advancing precision medicine and providing personalized treatments and other types of interventions. However, these opportunities come with privacy concerns, and data misuse could potentially lead to privacy infringement for individuals and their blood relatives. With the rapid growth and increased availability of genomic datasets, understanding the current genome privacy landscape and identifying the challenges in developing effective privacy-protecting solutions are imperative. In this work, we provide an overview of major privacy threats identified by the research community and examine the privacy challenges in the context of emerging direct-to-consumer genetic-testing applications. We additionally present general privacy-protection techniques for genomic data sharing and their potential applications in direct-to-consumer genomic testing and forensic analyses. Finally, we discuss limitations in current privacy-protection methods, highlight possible mitigation strategies and suggest future research opportunities for advancing genomic data sharing.
| S-EPMC7761157 | biostudies-literature
Project description:BACKGROUND:Sharing research data uses resources effectively; enables large, diverse data sets; and supports rigor and reproducibility. However, sharing such data increases privacy risks for participants who may be re-identified by linking study data to outside data sets. These risks have been investigated for genetic and medical records but rarely for environmental data. OBJECTIVES:We evaluated how data in environmental health (EH) studies may be vulnerable to linkage and we investigated, in a case study, whether environmental measurements could contribute to inferring latent categories (e.g., geographic location), which increases privacy risks. METHODS:We identified 12 prominent EH studies, reviewed the data types collected, and evaluated the availability of outside data sets that overlap with study data. With data from the Household Exposure Study in California and Massachusetts and the Green Housing Study in Boston, Massachusetts, and Cincinnati, Ohio, we used k-means clustering and principal component analysis to investigate whether participants' region of residence could be inferred from measurements of chemicals in household air and dust. RESULTS:All 12 studies included at least two of five data types that overlap with outside data sets: geographic location (9 studies), medical data (9 studies), occupation (10 studies), housing characteristics (10 studies), and genetic data (7 studies). In our cluster analysis, participants' region of residence could be inferred with 80%-98% accuracy using environmental measurements with original laboratory reporting limits. DISCUSSION:EH studies frequently include data that are vulnerable to linkage with voter lists, tax and real estate data, professional licensing lists, and ancestry websites, and exposure measurements may be used to identify subgroup membership, increasing likelihood of linkage. Thus, unsupervised sharing of EH research data potentially raises substantial privacy risks. Empirical research can help characterize risks and evaluate technical solutions. Our findings reinforce the need for legal and policy protections to shield participants from potential harms of re-identification from data sharing. https://doi.org/10.1289/EHP4817.
| S-EPMC7015543 | biostudies-literature
Project description:[Figure: see text].
| S-EPMC8664254 | biostudies-literature
Project description:PURPOSE:Privacy-protecting analytic and data-sharing methods that minimize the disclosure risk of sensitive information are increasingly important due to the growing interest in utilizing data across multiple sources. We conducted a simulation study to examine how avoiding sharing individual-level data in a distributed data network can affect analytic results. METHODS:The base scenario had four sites of varying sizes with 5% outcome incidence, 50% treatment prevalence, and seven confounders. We varied treatment prevalence, outcome incidence, treatment effect, site size, number of sites, and covariate distribution. Confounding adjustment was conducted using propensity score or disease risk score. We compared analyses of three types of aggregate-level data requested from sites: risk-set, summary-table, or effect-estimate data (meta-analysis) with benchmark results of analysis of pooled individual-level data. We assessed bias and precision of hazard ratio estimates as well as the accuracy of standard error estimates. RESULTS:All the aggregate-level data-sharing approaches, regardless of confounding adjustment methods, successfully approximated pooled individual-level data analysis in most simulation scenarios. Meta-analysis showed minor bias when using inverse probability of treatment weights (IPTW) in infrequent exposure (5%), rare outcome (0.01%), and small site (5,000 patients) settings. SE estimates became less accurate for IPTW risk-set approach with less frequent exposure and for propensity score-matching meta-analysis approach with rare outcomes. CONCLUSIONS:Overall, we found that we can avoid sharing individual-level data and obtain valid results in many settings, although care must be taken with meta-analysis approach in infrequent exposure and rare outcome scenarios, particularly when confounding adjustment is performed with IPTW.
| S-EPMC6135666 | biostudies-literature