Implementing a hash-based privacy-preserving record linkage tool in the OneFlorida clinical research network.
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ABSTRACT:
SUBMITTER: Bian J
PROVIDER: S-EPMC6994009 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:ObjectiveRecord linkage to integrate uncoordinated databases is critical in biomedical research using Big Data. Balancing privacy protection against the need for high quality record linkage requires a human-machine hybrid system to safely manage uncertainty in the ever changing streams of chaotic Big Data.MethodsIn the computer science literature, private record linkage is the most published area. It investigates how to apply a known linkage function safely when linking two tables. However, in practice, the linkage function is rarely known. Thus, there are many data linkage centers whose main role is to be the trusted third party to determine the linkage function manually and link data for research via a master population list for a designated region. Recently, a more flexible computerized third-party linkage platform, Secure Decoupled Linkage (SDLink), has been proposed based on: (1) decoupling data via encryption, (2) obfuscation via chaffing (adding fake data) and universe manipulation; and (3) minimum information disclosure via recoding.ResultsWe synthesize this literature to formalize a new framework for privacy preserving interactive record linkage (PPIRL) with tractable privacy and utility properties and then analyze the literature using this framework.ConclusionsHuman-based third-party linkage centers for privacy preserving record linkage are the accepted norm internationally. We find that a computer-based third-party platform that can precisely control the information disclosed at the micro level and allow frequent human interaction during the linkage process, is an effective human-machine hybrid system that significantly improves on the linkage center model both in terms of privacy and utility.
| S-EPMC3932473 | biostudies-literature
Project description:BackgroundPrivacy preserving record linkage (PPRL) methods using Bloom filters have shown promise for use in operational linkage settings. However real-world evaluations are required to confirm their suitability in practice.MethodsAn extract of records from the Western Australian (WA) Hospital Morbidity Data Collection 2011-2015 and WA Death Registrations 2011-2015 were encoded to Bloom filters, and then linked using privacy-preserving methods. Results were compared to a traditional, un-encoded linkage of the same datasets using the same blocking criteria to enable direct investigation of the comparison step. The encoded linkage was carried out in a blinded setting, where there was no access to un-encoded data or a 'truth set'.ResultsThe PPRL method using Bloom filters provided similar linkage quality to the traditional un-encoded linkage, with 99.3% of 'groupings' identical between privacy preserving and clear-text linkage.ConclusionThe Bloom filter method appears suitable for use in situations where clear-text identifiers cannot be provided for linkage.
| S-EPMC8761329 | biostudies-literature
Project description:Recent advances in technology have led to an explosion of data in virtually all domains of our lives. Modern biomedical devices can acquire a large number of physical readings from patients. Often, these readings are stored in the form of time series data. Such time series data can form the basis for important research to advance healthcare and well being. Due to several considerations including data size, patient privacy, etc., the original, full data may not be available to secondary parties or researchers. Instead, suppose that a subset of the data is made available. A fast and reliable record linkage algorithm enables us to accurately match patient records in the original and subset databases while maintaining privacy. The problem of record linkage when the attributes include time series has not been studied much in the literature. We introduce two main contributions in this paper. First, we propose a novel, very efficient, and scalable record linkage algorithm that is employed on time series data. This algorithm is 400× faster than the previous work. Second, we introduce a privacy preserving framework that enables health institutions to safely release their raw time series records to researchers with bare minimum amount of identifying information.
| S-EPMC9968283 | biostudies-literature
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
Project description:MotivationRecord Linkage has versatile applications in real-world data analysis contexts, where several datasets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records.ResultsWe develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10 000 records can be done in 48 s over a heavily delayed (100 ms) network connection, or 3.9 s with a low-latency connection.Availability and implementationThe source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC8896632 | biostudies-literature
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
Project description:Introduction:Available and practical methods for privacy preserving linkage have shortcomings: methods utilising anonymous linkage codes provide limited accuracy while methods based on Bloom filters have proven vulnerable to frequency-based attacks. Objectives:In this paper, we present and evaluate a novel protocol that aims to meld both the accuracy of the Bloom filter method with the privacy achievable through the anonymous linkage code methodology. Methods:The protocol involves creating multiple match-keys for each record, with the composition of each match-key depending on attributes of the underlying datasets being compared. The protocol was evaluated through de-duplication of four administrative datasets and two synthetic datasets; the 'answers' outlining which records belonged to the same individual were known for each dataset. The results were compared against results achieved with un-encoded linkage and other privacy preserving techniques on the same datasets. Results:The multiple match-key protocol presented here achieved high quality across all datasets, performing better than record-level Bloom filters and the SLK, but worse than field-level Bloom filters. Conclusion:The presented method provides high linkage quality while avoiding the frequency based attacks that have been demonstrated against the Bloom filter approach. The method appears promising for real world use.
| S-EPMC7482515 | biostudies-literature
Project description:BackgroundThere is substantial prior research on the perspectives of patients on the use of health information for research. Numerous communication barriers challenge transparency between researchers and data participants in secondary database research (eg, waiver of informed consent and knowledge gaps). Individual concerns and misconceptions challenge the trust in researchers among patients despite efforts to protect data. Technical software used to protect research data can further complicate the public's understanding of research. For example, MiNDFIRL (Minimum Necessary Disclosure For Interactive Record Linkage) is a prototype software that can be used to enhance the confidentiality of data sets by restricting disclosures of identifying information during the record linkage process. However, software, such as MiNDFIRL, which is used to protect data, must overcome the aforementioned communication barriers. One proposed solution is the creation of an interactive web-based frequently asked question (FAQ) template that can be adapted and used to communicate research issues to data subjects.ObjectiveThis study aims to improve communication with patients and transparency about how complex software, such as MiNDFIRL, is used to enhance privacy in secondary database studies to maintain the public's trust in researchers.MethodsA Delphi technique with 3 rounds of the survey was used to develop the FAQ document to communicate privacy issues related to a generic secondary database study using the MiNDFIRL software. The Delphi panel consisted of 38 patients with chronic health conditions. We revised the FAQ between Delphi rounds and provided participants with a summary of the feedback. We adopted a conservative consensus threshold of less than 10% negative feedback per FAQ section.ResultsWe developed a consensus language for 21 of the 24 FAQ sections. Participant feedback demonstrated preference differences (eg, brevity vs comprehensiveness). We adapted the final FAQ into an interactive web-based format that 94% (31/33) of the participants found helpful or very helpful. The template FAQ and MiNDFIRL source code are available on GitHub. The results indicate the following patient communication considerations: patients have diverse and varied preferences; the tone is important but challenging; and patients want information on security, identifiers, and final disposition of information.ConclusionsThe findings of this study provide insights into what research-related information is useful to patients and how researchers can communicate such information. These findings align with the current understanding of health literacy and its challenges. Communication is essential to transparency and ethical data use, yet it is exceedingly challenging. Developing FAQ template language to accompany a complex software may enable researchers to provide greater transparency when informed consent is not possible.
| S-EPMC7772068 | biostudies-literature
Project description:The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we propose a privacy-preserving framework which utilizes PCA to assign individuals to populations across multiple collaborators as part of the population stratification step. In our proposed client-server-based scheme, we initially let the server train a global PCA model on a publicly available genomic dataset which contains individuals from multiple populations. The global PCA model is later used to reduce the dimensionality of the local data by each collaborator (client). After adding noise to achieve local differential privacy (LDP), the collaborators send metadata (in the form of their local PCA outputs) about their research datasets to the server, which then aligns the local PCA results to identify the genetic differences among collaborators' datasets. Our results on real genomic data show that the proposed framework can perform population stratification analysis with high accuracy while preserving the privacy of the research participants.
| S-EPMC10311306 | biostudies-literature
Project description:OBJECTIVE:To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS:We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS:HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION:HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION:We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.
| S-EPMC7025358 | biostudies-literature