Статті в журналах: "Pseudonymisation"

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Drąg, Paweł, and Mateusz Szymura. "TECHNICAL AND LEGAL ASPECTS OF DATABASE'S SECURITY IN THE LIGHT OF IMPLEMENTATION OF GENERAL DATA PROTECTION REGULATION." CBU International Conference Proceedings 6 (September 25, 2018): 1056–61. http://dx.doi.org/10.12955/cbup.v6.1294.

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In the modern era, information is not only a valuable commodity, but also a potential source of threat, especially when it comes to personal data. The implementation of the General Data Protection Regulation seeks to unify regulations and safeguards in a same manner across the EU. The following paper surveys how the legal aspects of GDPR influence the existing technical framework of databases containing personal data. In this research we want to show if the already existing technical infrastructure and safeguards implemented in databases containing personal data are sufficient and if not, if implementing new ways of protecting of data will require creating entire new system of databases or only changing of existing framework. Therefore, we combine an analysis of legal texts with a technical analysis of existing and newly implemented safeguards. While the GDPR doesn’t answer what safeguards should be implemented (in the spirit of technological neutrality), the notion of pseudonymisation of the data is strongly advocated through the Regulation. In our paper we tried to show the algorithm, which create a pseudonymisation function that can change personal data into generic data with the possibility to reverse that process ad utilise data after de-pseudonymisation. Implementing safeguards based on the following function create a more safe environment for data safekeeping, while give nearly immediate access to data for authorised person, who can reverse pseudonymisation and transform generic data once more into personal data.

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Claerhout, B., F. De Meyer, and G. J. E. De Moor. "Privacy Enhancing Techniques." Methods of Information in Medicine 42, no. 02 (2003): 148–53. http://dx.doi.org/10.1055/s-0038-1634326.

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Summary Objectives: To introduce some of the privacy protection problems related to genomics based medicine and to highlight the relevance of Trusted Third Parties (TTPs) and of Privacy Enhancing Techniques (PETs) in the restricted context of clinical research and statistics. Methods: Practical approaches based on two different pseudonymisation models, both for batch and interactive data collection and exchange, are described and analysed. Results and Conclusions: The growing need of managing both clinical and genetic data raises important legal and ethical challenges. Protecting human rights in the realm of privacy, while optimising research potential and other statistical activities is a challenge that can easily be overcome with the assistance of a trust service provider offering advanced privacy enabling/enhancing solutions. As such, the use of pseudonymisation and other innovative Privacy Enhancing Techniques can unlock valuable data sources.

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Noé, Paul-Gauthier, Andreas Nautsch, Nicholas Evans, Jose Patino, Jean-François Bonastre, Natalia Tomashenko, and Driss Matrouf. "Towards a unified assessment framework of speech pseudonymisation." Computer Speech & Language 72 (March 2022): 101299. http://dx.doi.org/10.1016/j.csl.2021.101299.

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Goldstein, Harvey, and Katie Harron. "‘Pseudonymisation at source’ undermines accuracy of record linkage." Journal of Public Health 40, no. 2 (May 15, 2018): 219–20. http://dx.doi.org/10.1093/pubmed/fdy083.

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Smith, D. "Secure pseudonymisation for privacy-preserving probabilistic record linkage." Journal of Information Security and Applications 34 (June 2017): 271–79. http://dx.doi.org/10.1016/j.jisa.2017.01.002.

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van Gastel, Bernard E., Bart Jacobs, and Jean Popma. "Data Protection Using Polymorphic Pseudonymisation in a Large-Scale Parkinson’s Disease Study." Journal of Parkinson's Disease 11, s1 (July 16, 2021): S19—S25. http://dx.doi.org/10.3233/jpd-202431.

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This paper describes an advanced form of pseudonymisation in a large cohort study on Parkinson’s disease, called Personalized Parkinson Project (PPP). The study collects various forms of biomedical data of study participants, including data from wearable devices with multiple sensors. The participants are all from the Netherlands, but the data will be usable by research groups worldwide on the basis of a suitable data use agreement. The data are pseudonymised, as required by Europe’s General Data Protection Regulation (GDPR). The form of pseudonymisation that is used in this Parkinson project is based on cryptographic techniques and is ‘polymorphic’: it gives each participating research group its own ‘local’ pseudonyms. Still, the system is globally consistent, in the sense that if one research group adds data to PPP under its own local pseudonyms, the data become available for other groups under their pseudonyms. The paper gives an overview how this works, without going into the cryptographic details.

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Heurix, Johannes, Stefan Fenz, Antonio Rella, and Thomas Neubauer. "Recognition and pseudonymisation of medical records for secondary use." Medical & Biological Engineering & Computing 54, no. 2-3 (June 4, 2015): 371–83. http://dx.doi.org/10.1007/s11517-015-1322-7.

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Mayfield, Kate. "Pseudonymisation: a 20-year-old idea never seemed so timely." Journal of Direct, Data and Digital Marketing Practice 17, no. 4 (June 2016): 222–26. http://dx.doi.org/10.1057/s41263-016-0005-x.

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Verhenneman, G., K. Claes, J. J. Derèze, P. Herijgers, C. Mathieu, F. E. Rademakers, R. Reyda, and M. Vanautgaerden. "How GDPR Enhances Transparency and Fosters Pseudonymisation in Academic Medical Research." European Journal of Health Law 27, no. 1 (March 4, 2020): 35–57. http://dx.doi.org/10.1163/15718093-12251009.

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Abstract The European General Data Protection Regulation (GDPR) has dotted the i’s and crossed the t’s in the context of academic medical research. One year into GDPR, it is clear that a change of mind and the uptake of new procedures is required. Research organisations have been looking at the possibility to establish a code-of-conduct, good practices and/or guidelines for researchers that translate GDPR’s abstract principles to concrete measures suitable for implementation. We introduce a proposal for the implementation of GDPR in the context of academic research which involves the processing of health related data, as developed by a multidisciplinary team at the University Hospitals Leuven. The proposal is based on three elements, three stages and six specific safeguards. Transparency and pseudonymisation are considered key to find a balance between the need for researchers to collect and analyse personal data and the increasing wish of data subjects for informational control.

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Béguin-Faynel, Céline. "L’open data judiciaire et les données personnelles : pseudonymisation et risque de ré-identification." Archives de philosophie du droit Tome 60, no. 1 (May 20, 2018): 153–81. http://dx.doi.org/10.3917/apd.601.0168.

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Singleton, P., J. Milan, J. MacKay, D. Detmer, A. Rector, D. Ingram, and D. Kalra. "Security and Confidentiality Approach for the Clinical E-Science Framework (CLEF)." Methods of Information in Medicine 44, no. 02 (2005): 193–97. http://dx.doi.org/10.1055/s-0038-1633945.

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Summary Objectives: CLEF is an MRC sponsored project in the E-Science programme that aims to establish methodologies and a technical infrastructure for the next generation of integrated clinical and bioscience research. Methods: The heart of the CLEF approach to this challenge is to design and develop a pseudonymised repository of histories of cancer patients that can be accessed by researchers. Robust mechanisms and policies have been developed to ensure that patient privacy and confidentiality are preserved while delivering a repository of such medically rich information for the purposes of scientific research. Results: This paper summarises the overall approach adopted by CLEF to meet data protection requirements, including the data flows, pseudonymisation measures and additional monitoring policies that are currently being developed. Conclusion: Once evaluated, it is hoped that the CLEF approach can serve as a model for other distributed electronic health record repositories to be accessed for research.

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Hagger-Johnson, Gareth, Katie Harron, Tom Fleming, Ruth Gilbert, Harvey Goldstein, Rebecca Landy, and Roger C. Parslow. "Data linkage errors in hospital administrative data when applying a pseudonymisation algorithm to paediatric intensive care records." BMJ Open 5, no. 8 (August 2015): e008118. http://dx.doi.org/10.1136/bmjopen-2015-008118.

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Choi, Kwan Sig. "Personal Information Protection Crisis Management in Big Data." Crisis and Emergency Management: Theory and Praxis 17, no. 11 (November 30, 2021): 95–108. http://dx.doi.org/10.14251/crisisonomy.2021.17.11.95.

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Due to rapidly-rising development of ICT the era of Big Data comes in modern lives and Big Data are deeply located at our everyday lives. Personal informations are integrated, used widely at the many aspects including public and private sectors. Big Data including personal informations in itself become giant industries in modern business nowadays. Big Data that personal informations have been integrated may cause serious infringements of personal informations. According to Personal Information Protection Act in the cases one collects or uses personal informations one has to obtain consent by concerned party of personal information. But it is not easy to obtain consent that is necessary to collect and use Big Data creating massively and automatically by ICT instruments. In the aspect of management of Big Data, to protect personal informations throughly, pseudonymisation, anonymisation of personal information have to be permitted in the Big Data collections and uses. And possibility of fake manipulation about Big Data may always exists.

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Class, Barbara, Miguel de Bruyne, Claire Wuillemin, Dimitri Donzé, and Jean-Blaise Claivaz. "Towards Open Science for the Qualitative Researcher: From a Positivist to an Open Interpretation." International Journal of Qualitative Methods 20 (January 2021): 160940692110346. http://dx.doi.org/10.1177/16094069211034641.

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This reflection by a qualitative researcher stems from a concrete experience with data handling in a funded research project. The researcher followed Open Research Data guidelines and found optimal solutions to pseudonymise data, but this later evolved into a deep epistemological questioning on praxis. During the first phase of the project, a tailor-made software was developed with help from librarians and an IT professional to automate the pseudonymisation of the 150 data chunks generated by 16 students, 3 tutors and 3 decision makers. In the second phase of the project, this experience sparked questions about the meaning of such data handling and interpretations of Open Science, which led the researcher to suggest a framework for the professional development of qualitative researchers in their understanding of Open Science. The article raises awareness of normative frameworks in institutional data handling practices and calls for active contributions to defining qualitative research in an Open Science perspective, particularly taking as a reference the recent draft recommendation by UNESCO (2020)

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Sukhorolskyi, Petro, and Valeriia Hutsaliuk. "Processing of Genetic Data under GDPR: Unresolved Conflict of Interests." Masaryk University Journal of Law and Technology 14, no. 2 (September 23, 2020): 151–76. http://dx.doi.org/10.5817/mujlt2020-2-1.

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Over the last decades, developments in the fields of genetics and bioinformatics caused a marked increase in the processing of human genetic data by various companies and institutions. This results in the adoption of several international documents and the emergence of legal norms on the protection of genetic data. The paper examines how and to what extent the interests and rights of the data subject with regard to the processing of genetic data are protected in the European Union. It is concluded that under the GDPR this task is implemented through classifying genetic data as sensitive, reliance on anonymisation and pseudonymisation, as well as introduction of the procedure of data protection impact assessment. Nevertheless, given the unique characteristics of genetic data distinguishing them from other categories of personal data, these measures cannot be regarded as sufficient and effective. The paper argues that current EU data protection legislation creates favourable conditions for genetic research, thereby ensuring particular public interests, but does not establish a special regime for genetic data processing appropriate to potential threats in this field and risks to the rights of data subjects.

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Atanassova, Iana, Marc Bertin, and Mariannig Le Béchec. "Sécuriser le traitement des traces numériques dans le cadre du Règlement général sur la protection des données (RGPD) : anonymisation et pseudonymisation." I2D - Information, données & documents 1, no. 1 (2019): 55. http://dx.doi.org/10.3917/i2d.191.0055.

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Hagger-Johnson, Gareth, Katie Harron, Harvey Goldstein, Robert Aldridge, and Ruth Gilbert. "Probabilistic linkage to enhance deterministic algorithms and reduce data linkage errors in hospital administrative data." Journal of Innovation in Health Informatics 24, no. 2 (June 30, 2017): 234. http://dx.doi.org/10.14236/jhi.v24i2.891.

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BackgroundThe pseudonymisation algorithm used to link together episodes of care belonging to the same patients in England (HESID) has never undergone any formal evaluation, to determine the extent of data linkage error.ObjectiveTo quantify improvements in linkage accuracy from adding probabilistic linkage to existing deterministic HESID algorithms.MethodsInpatient admissions to NHS hospitals in England (Hospital Episode Statistics, HES) over 17 years (1998 to 2015) for a sample of patients (born 13/28th of months in 1992/1998/2005/2012). We compared the existing deterministic algorithm with one that included an additional probabilistic step, in relation to a reference standard created using enhanced probabilistic matching with additional clinical and demographic information. Missed and false matches were quantified and the impact on estimates of hospital readmission within one year were determined.ResultsHESID produced a high missed match rate, improving over time (8.6% in 1998 to 0.4% in 2015). Missed matches were more common for ethnic minorities, those living in areas of high socio-economic deprivation, foreign patients and those with ‘no fixed abode’. Estimates of the readmission rate were biased for several patient groups owing to missed matches, which was reduced for nearly all groups. ConclusionProbabilistic linkage of HES reduced missed matches and bias in estimated readmission rates, with clear implications for commissioning, service evaluation and performance monitoring of hospitals. The existing algorithm should be modified to address data linkage error, and a retrospective update of the existing data would address existing linkage errors and their implications.

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De Lusignan, Simon. "Effective pseudonymisation and explicit statements of public interest to ensure the benefits of sharing health data for research, quality improvement and health service management outweigh the risks." Journal of Innovation in Health Informatics 21, no. 2 (May 16, 2014): 61–63. http://dx.doi.org/10.14236/jhi.v21i2.68.

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Crossfield, Samantha S. R., Kieran Zucker, Paul Baxter, Penny Wright, Jon Fistein, Alex F. Markham, Mark Birkin, Adam W. Glaser, and Geoff Hall. "A data flow process for confidential data and its application in a health research project." PLOS ONE 17, no. 1 (January 21, 2022): e0262609. http://dx.doi.org/10.1371/journal.pone.0262609.

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Background The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. Methods We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. Results We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. Conclusions Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.

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Purificato, Erasmo, Sabine Wehnert, and Ernesto William De Luca. "Dynamic Privacy-Preserving Recommendations on Academic Graph Data." Computers 10, no. 9 (August 25, 2021): 107. http://dx.doi.org/10.3390/computers10090107.

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In the age of digital information, where the internet and social networks, as well as personalised systems, have become an integral part of everyone’s life, it is often challenging to be aware of the amount of data produced daily and, unfortunately, of the potential risks caused by the indiscriminate sharing of personal data. Recently, attention to privacy has grown thanks to the introduction of specific regulations such as the European GDPR. In some fields, including recommender systems, this has inevitably led to a decrease in the amount of usable data, and, occasionally, to significant degradation in performance mainly due to information no longer being attributable to specific individuals. In this article, we present a dynamic privacy-preserving approach for recommendations in an academic context. We aim to implement a personalised system capable of protecting personal data while at the same time allowing sensible and meaningful use of the available data. The proposed approach introduces several pseudonymisation procedures based on the design goals described by the European Union Agency for Cybersecurity in their guidelines, in order to dynamically transform entities (e.g., persons) and attributes (e.g., authored papers and research interests) in such a way that any user processing the data are not able to identify individuals. We present a case study using data from researchers of the Georg Eckert Institute for International Textbook Research (Brunswick, Germany). Building a knowledge graph and exploiting a Neo4j database for data management, we first generate several pseudoN-graphs, being graphs with different rates of pseudonymised persons. Then, we evaluate our approach by leveraging the graph embedding algorithm node2vec to produce recommendations through node relatedness. The recommendations provided by the graphs in different privacy-preserving scenarios are compared with those provided by the fully non-pseudonymised graph, considered as the baseline of our evaluation. The experimental results show that, despite the structural modifications to the knowledge graph structure due to the de-identification processes, applying the approach proposed in this article allows for preserving significant performance values in terms of precision.

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Bartholomäus, Sebastian, Yannik Siegert, Hans Werner Hense, and Oliver Heidinger. "Secure Linking of Data from Population-Based Cancer Registries with Healthcare Data to Evaluate Screening Programs." Das Gesundheitswesen 82, S 02 (December 10, 2019): S131—S138. http://dx.doi.org/10.1055/a-1031-9526.

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Abstract Background The evaluation of population-based screening programs, like the German Mammography Screening Program (MSP), requires collection and linking data from population-based cancer registries and other sources of the healthcare system on a case- specific level. To link such sensitive data, we developed a method that is compliant with German data protection regulations and does not require written individual consent. Methods Our method combines a probabilistic record linkage on encrypted identifying data with ‘blinded anonymisation’. It ensures that all data either are encrypted or have a defined and measurable degree of anonymity. The data sources use a software to transform plain-text identifying data into a set of irreversibly encrypted person cryptograms, while the evaluation attributes are aggregated in multiple stages and are reversibly encrypted. A pseudonymisation service encrypts the person cryptograms into record assignment numbers and a downstream data-collecting centre uses them to perform the probabilistic record linkage. The blinded anonymisation solves the problem of quasi-identifiers within the evaluation data. It allows selecting a specific set of the encrypted aggregations to produce data export with ensured k-anonymity, without any plain-text information. These data are finally transferred to an evaluation centre where they are decrypted and analysed. Our approach allows creating several such generalisations, with different resulting suppression rates allowing dynamic balance information depth with privacy protection and also highlights how this affects data analysability. Results German data protection authorities approved our concept for the evaluation of the impact of the German MSP on breast cancer mortality. We implemented a prototype and tested it with 1.5 million simulated records, containing realistically distributed identifying data, calculated different generalisations and the respective suppression rates. Here, we also discuss limitations for large data sets in the cancer registry domain, as well as approaches for further improvements like l-diversity and how to reduce the amount of manual post-processing. Conclusion Our approach enables secure linking of data from population-based cancer registries and other sources of the healthcare system. Despite some limitations, it enables evaluation of the German MSP program and can be generalised to be applicable to other projects.

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Yang, GiJin. "A critical review of the bill of Credit Information Act introduced by Kim ByungWook, member of Korean Assembly - focused on the pseudonymisation, anonymisation and allowance of data collection disclosed to SNS without data subjectsʼ agreements -". Commercial Law Review 38, № 2 (31 серпня 2019): 249–76. http://dx.doi.org/10.21188/clr.38.2.7.

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"Privacy Preservation of Sensitive Data using Polymorphic Encryption and Cryptographic Techniques." International Journal of Innovative Technology and Exploring Engineering 8, no. 12S (December 26, 2019): 433–40. http://dx.doi.org/10.35940/ijitee.l1108.10812s19.

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The compilation and analysis of health records on a big data scale is becoming an essential approach to understand problematical diseases. In order to gain new insights it is important that researchers can cooperate: they will have to access each other's data and contribute to the data sets. In many cases, such health records involves privacy sensitive data about patients. Patients should be cautious to count on preservation of their privacy and on secure storage of their data. Polymorphic encryption and Pseudonymisation, form a narrative approach for the management of sensitive information, especially in health care. The conventional encryptionsystem is rather inflexible: once scrambled, just one key can be utilized to unscramble the information. This inflexibility is turning into an each more noteworthy issue with regards to huge information examination, where various gatherings who wish to research some portion of an encoded informational index all need the one key for decoding. Polymorphic encryption is another cryptographic strategy that tackles these issues. Together with the related procedure of polymorphic pseudonymisation new security and protection assurances can be given which are fundamental in zones, for example, (customized) wellbeing area, medicinal information accumulation by means of self-estimation applications, and all the more by and large in protection inviting character the board and information examination.Encryption, pseudonymization and anonymization are some of the importanttechniques that facilitate the usders on security of sensitive data, and ensure compliance both from an Data Regulation act and any other information security act like Health Insurance Portability and Accountability Act - (HIPAA) regulations.

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Pedrosa, Micael, Andre Zuquete, and Carlos Costa. "A pseudonymisation protocol with implicit and explicit consent routes for health records in federated ledgers." IEEE Journal of Biomedical and Health Informatics, 2020, 1. http://dx.doi.org/10.1109/jbhi.2020.3028454.

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Gött, Robert, Sebastian Stäubert, Alexander Strübing, Alfred Winter, Angela Merzweiler, Björn Bergh, Knut Kaulke, Thomas Bahls, Wolfgang Hoffmann, and Martin Bialke. "3LGM2IHE: Requirements for data-protection-compliant research infrastructures. A systematic comparison of theory and practice-oriented implementation." Methods of Information in Medicine, September 23, 2022. http://dx.doi.org/10.1055/a-1950-2791.

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Objectives The TMF (Technology, Methods, and Infrastructure for Networked Medical Research) Data Protection Guide (TMF-DP) makes path-breaking recommendations on the subject of data protection in research projects. It includes comprehensive requirements for applications such as patient lists, pseudonymisation services and consent management services. Nevertheless, it lacks a structured, categorised list of requirements for simplified application in research projects and systematic evaluation. The DFG-founded 3LGM2IHE project ("Three-layer Graph-based meta model – Integrating the Healthcare Enterprise (IHE)") aims to define modeling paradigms and implement modeling tools for planning healthcare information systems. In addition, one of the goals is to create and publish 3LGM² information system architecture design patterns (short “design patterns”) for the community as design models in terms of a framework. A structured list of data protection-related requirements based depicted from the TMF-DP is a precondition to integrate functions (3LGM² Domain Layer) and building blocks (3LGM² Logical Tool Layer) in 3LGM² design patterns. Methods In order to structure the continuous text of the TMF-DP, requirement types were defined in a first step. In a second step dependencies and delineations of the definitions were identified. In a third step, the requirements from the TMF-DP were systematically extracted. Based on the identified lists of requirements, a fourth step included the comparison of the identified requirements with exemplary open source tools as provided by the "Independent Trusted Third Party of the University Medicine Greifswald" (TTP tools). Results As a result, four lists of requirements were created, which contain requirements for the 'patient list', the 'pseudonymisation service' and the 'consent management', as well as cross-component requirements from the TMF-DP chapter 6 in a structured form. Further to requirements (A), possible variants (B) of implementations (to fulfill a single requirement) and recommendations (C) were identified. A comparison of the requirements lists with the functional scopes of the open source tools E-PIX (record linkage), gPAS (pseudonym management) and gICS (consent management) has shown that these fulfill more than 80% of the requirements. Conclusions A structured set of data protection-related requirements facilitates a systematic evaluation of implementations with respect to the fulfillment of the TMF-DP guidelines. These re-usable lists provide a decision aid for the selection of suitable tools for new research projects. As a result, these lists form the basis for the development of data protection-related 3LGM²-design patterns as part of the 3LGM2IHE-project.

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Lewer, Dan, Tom Bourne, Abraham George, Gerrard Abi-Aad, Clint Taylor, and Julie George. "Data Resource: the Kent Integrated Dataset (KID)." International Journal of Population Data Science 3, no. 1 (April 25, 2018). http://dx.doi.org/10.23889/ijpds.v3i1.427.

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IntroductionElectronic healthcare records from the UK are accessible to researchers via a number of platforms, but these platforms typically include data from a limited subset of health and care services. The Kent Integrated Dataset (KID) aims to provide insight into system-wide health and care utilisation for the whole population of Kent and Medway. MethodsThe KID uses pseudonymisation-at-source to link patient-level records from services including general practices, hospitals, community health services and social care. The design and governance of the dataset is led by local authorities, health commissioners and service providers. ResultsA population-level dataset has been developed, including data from April 2014 onwards. Data providers add new data on a monthly basis. The KID has been used to understand the costs associated with frailty, estimate the prevalence of rare conditions and compare the risk of non-elective hospitalisation between general practices. ConclusionThe KID is a unique and rich dataset available to researchers who are investigating a broad range of public health questions. It provides system-level insight into patient journeys and care utilisation and supports commissioning based on patient needs.

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Scheibner, James, Marcello Ienca, Sotiria Kechagia, Juan Ramon Troncoso-Pastoriza, Jean Louis Raisaro, Jean-Pierre Hubaux, Jacques Fellay, and Effy Vayena. "Data protection and ethics requirements for multisite research with health data: a comparative examination of legislative governance frameworks and the role of data protection technologies†." Journal of Law and the Biosciences 7, no. 1 (January 2020). http://dx.doi.org/10.1093/jlb/lsaa010.

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Abstract Personalised medicine can improve both public and individual health by providing targeted preventative and therapeutic healthcare. However, patient health data must be shared between institutions and across jurisdictions for the benefits of personalised medicine to be realised. Whilst data protection, privacy, and research ethics laws protect patient confidentiality and safety they also may impede multisite research, particularly across jurisdictions. Accordingly, we compare the concept of data accessibility in data protection and research ethics laws across seven jurisdictions. These jurisdictions include Switzerland, Italy, Spain, the United Kingdom (which have implemented the General Data Protection Regulation), the United States, Canada, and Australia. Our paper identifies the requirements for consent, the standards for anonymisation or pseudonymisation, and adequacy of protection between jurisdictions as barriers for sharing. We also identify differences between the European Union and other jurisdictions as a significant barrier for data accessibility in cross jurisdictional multisite research. Our paper concludes by considering solutions to overcome these legislative differences. These solutions include data transfer agreements and organisational collaborations designed to `front load' the process of ethics approval, so that subsequent research protocols are standardised. We also allude to technical solutions, such as distributed computing, secure multiparty computation and homomorphic encryption.

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Katschnig, H., G. Endel, F. Endel, P. Filzmoser, and B. Weibold. "Identifying psychiatric patients' pathways of care by record linkage after pseudonymisation: linking inpatient and outpatient data for the total population of a province of Austria." Psychiatrische Praxis 38, S 01 (April 14, 2011). http://dx.doi.org/10.1055/s-0031-1277820.

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Rodriguez, Aryelly, Christopher Tuck, Marshall F. Dozier, Stephanie C. Lewis, Sandra Eldridge, Tracy Jackson, Alastair Murray, and Christopher J. Weir. "Current recommendations/practices for anonymising data from clinical trials in order to make it available for sharing: A scoping review." Clinical Trials, June 22, 2022, 174077452210874. http://dx.doi.org/10.1177/17407745221087469.

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Background/Aims There are increasing pressures for anonymised datasets from clinical trials to be shared across the scientific community, and differing recommendations exist on how to perform anonymisation prior to sharing. We aimed to systematically identify, describe and synthesise existing recommendations for anonymising clinical trial datasets to prepare for data sharing. Methods We systematically searched MEDLINE®, EMBASE and Web of Science from inception to 8 February 2021. We also searched other resources to ensure the comprehensiveness of our search. Any publication reporting recommendations on anonymisation to enable data sharing from clinical trials was included. Two reviewers independently screened titles, abstracts and full text for eligibility. One reviewer extracted data from included papers using thematic synthesis, which then was sense-checked by a second reviewer. Results were summarised by narrative analysis. Results Fifty-nine articles (from 43 studies) were eligible for inclusion. Three distinct themes are emerging: anonymisation, de-identification and pseudonymisation. The most commonly used anonymisation techniques are: removal of direct patient identifiers; and careful evaluation and modification of indirect identifiers to minimise the risk of identification. Anonymised datasets joined with controlled access was the preferred method for data sharing. Conclusions There is no single standardised set of recommendations on how to anonymise clinical trial datasets for sharing. However, this systematic review shows a developing consensus on techniques used to achieve anonymisation. Researchers in clinical trials still consider that anonymisation techniques by themselves are insufficient to protect patient privacy, and they need to be paired with controlled access.

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Bernal-Delgado, E., T. Engsig-Karup, F. Estupiñán-Romero, N. Sahlertz Kristiansen, and J. Bredmose Simonsen. "A Data Quality Framework for the European Health Data Space for secondary use." European Journal of Public Health 32, Supplement_3 (October 1, 2022). http://dx.doi.org/10.1093/eurpub/ckac129.498.

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Abstract A cornerstone in the development of the European Health Data Space for secondary use of data (EHDS2) is the design, implementation and assessment of a Data Quality Framework (DQF). Consistently, the Joint Action TEHDAS has a dedicated work program where, learning from others’ experiences across Europe and abroad, the work package is building the concepts and methods for such a DQF. The scope of this work program is to provide recommendation to the Member States and the European Commission on the concept of DQF to foster, where (institutions) the DQF should be implemented, when in data life cycle, how should be implemented and by whom. In terms of the concept, the DQF raises the importance of quality assurance procedures at data processor level and the level of quality of the data collections in terms of reliability, relevance, timeliness, coherence, coverage and completeness. When it comes to when along the data life cycle, DQF is expected operate when data needs harmonization at data processor level (ie, the effective application of interoperability standards), in the publication of the data sources (ie providing users knowledge on the provenance of data and the content of data source); or, when data sources have to be integrated and sensitive data pseudonymized (ie, the quality of the linkage and losses after pseudonymisation). Finally, when it comes to the methodology, TEHDAS suggests a three-fold approach - some quality measures in the DQF could be translated into legislation (eg, the requirement of regular auditing for a data processor to be a trusted party in the EDHS2); some could be kept as good-practices (eg, recommendation of archival procedures when a research project finalizes); and, under the assumption of continuous data quality improvement, an assessment, benchmarking and promotion methodology (eg, a grading system at data processor level).

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Hollinghurst, Joe, Richard Fry, Ashley Akbari, and Sarah Rodgers. "Using Residential Anonymous Linking Fields to Identify Vulnerable Populations in Administrative Data." International Journal of Population Data Science 3, no. 4 (September 5, 2018). http://dx.doi.org/10.23889/ijpds.v3i4.893.

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IntroductionDemographic profiling is an important aspect of anonymised healthcare research to identify the population of interest. Typically, administrative data is used in conjunction with patient registers to create cohorts, but it can be a time consuming process. We describe a method using routinely collected health data to identify vulnerable populations. Objectives and ApproachUsing existing longitudinal data and the Residential Anonymised Linking Field (RALF) we aim to identify institutions linked to vulnerable populations. We search for specific characteristics of these institutions including the age of occupants, number of current residents, and rate of change of occupants. We also aim to compare our method to a pseudonymised national registry for care homes to ensure it is accurate. This can effectively reduce the need for repeat pseudonymisation of institutions, which is both expensive and time consuming. ResultsTo implement our method we found the most recent address for living individuals aged 65-95. This produced 202,640 residences from 1,330,335. Of the 202,640 residences, 1347 had four or more cohabitants aged 65-95, and 172 had exactly three residents with ten or more distinct individuals registered over a 10-year period. Our final synthetic dataset therefore had 1519 unique potential care homes to compare to the national registry, which contains 1525 registered care homes. We can now link the synthetic dataset to individuals to flag their residential status, which may be a defining factor in their level of care. Furthermore, we can answer specific research questions relating to their residency, such as the time it takes to move to a care home following a hospital admission. Conclusion/ImplicationsBy using quantifiable characteristics of care homes we were able to create a synthetic care home register by searching existing data. This is a reproducible process that would be of particular benefit for projects where a registry is not available, or where time or cost would limit the availability.

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Oliveira, V. A., R. B. David, L. G. Mota, M. Barral-Netto, R. P. Carreiro, and D. F. Botelho. "A GDPR-compliant information system to improve community primary care in a middle income country." European Journal of Public Health 30, Supplement_5 (September 1, 2020). http://dx.doi.org/10.1093/eurpub/ckaa166.069.

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Abstract Background A strong primary health care (PHC) is associated to better overall health system results. Brazil has good results in PHC in the last decades, integrating 260,000 community health workers (CHW) in 43,000 family health teams (FHT), assisting 90 million people and delivering 500 million health activities yearly, such as home visits, consultations, colposcopy, etc. We address the challenges of incorporating CHA-produced data to official electronic health records, automate its analysis and promote information use by FHT to plan activities & prioritize individuals considering social determinants of health, clinical data and treatment plans. Our study developed a general data protection regulation (GDPR) compliant information system to improve community health agents and family health teams coordination of care in order to address this challenge. Methods The intervention was developed using UX techniques and combines Apps and Web dashboards, issuing digital alerts to the FHT and municipal health manager, regarding individual health status and pending care for each covered individual. The research used the “Monitoring and Evaluating Digital Health Interventions” toolbox by World Health Organization (WHO), and GDPR compliance was attained by terms of use acceptance, pseudonymisation and anonymization procedures. Results Stage 1 and Stage 2 Maturity tests with doctors, nurses and CHA showed good feasibility, usability and user satisfaction of the solution. UX and Qualitative Assessment are reported separately. Conclusions Results so far point that the solution is viable and acknowledged as useful by health professionals. Stage 3 (Pilot) will run in September 2020 in two different cities to test efficacy and health system adherence in real world setting. Digital health interventions are powerful tool to improve health care system performance, particularly in Primary Health Care. Key messages Digital Health Intervention are viable in Primary Care as long as they reduce health profesisonal burden and increase service quality. Brazil is a promising environment for Digital Health. Careful planning, development and deployment are essential in the process.

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Jones, Pete. "A data linkage strategy for producing census and population statistics from administrative data." International Journal of Population Data Science 1, no. 1 (April 19, 2017). http://dx.doi.org/10.23889/ijpds.v1i1.378.

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ObjectivesFollowing the recommendation of the National Statistician in 2014, it is intended that the 2021 Census of England and Wales will make far greater use of administrative data. The combined use of administrative and census data has the potential to enhance the quality and detail of outputs that can be produced in 2021. Furthermore, the government’s aspiration is that future censuses will be conducted with other sources of data. One of the major objectives of the next census is therefore to develop and test methods for producing a future alternative that relies primarily on administrative data and surveys. ApproachIn order to meet the objectives of the 2021 Census, a data linkage strategy is needed to support the statistical system for producing population statistics. Given the diverse uses of linked data in census statistical processing, each matching exercise will have different requirements in terms of scale, methodology and quality. This paper outlines a flexible methodological strategy that has been developed to meet those requirements, with examples of research that has been undertaken to date. ResultsResearch findings from a range of linkage exercises are presented with discussion around the methods used, the scale of the matching exercise and associated measures of quality. Examples include: Linking multiple administrative datasets to produce a ‘Statistical Population Dataset’ Linking to adjust for coverage errors using capture-recapture methods Generating multivariate tabulations from linked administrative and survey data Using linked administrative data to improve item imputation for missing values Linking of address records to assign Unique Property Reference Numbers Using administrative data to enhance the 2021 Census Address Register ConclusionCentral to the strategy is the need to develop a business model that can deliver linkage outputs to the required quality while still preserving the privacy of individuals’ data. We conclude that various procedural and technical options for preserving privacy can be incorporated within the framework of this strategy, including pseudonymisation, de-identification, trusted third party models and record indexing. The strategy developed will enable datasets to be linked to the required specifications. In addition, de-identified datasets can be held separately and integrated efficiently when required in the production of statistical outputs. The development of this strategy will continue in the run up to the 2021 Census, with the aim of incorporating its use in wider statistical output production, including population, business statistics and social surveys.

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Hooiveld, Mariette, Madelief Mollers, Stephanie Van Rooden, Robert A. Verheij, and Susan Hahné. "Establishing a National Syndromic Surveillance System among Asylum Seekers." Online Journal of Public Health Informatics 9, no. 1 (May 2, 2017). http://dx.doi.org/10.5210/ojphi.v9i1.7666.

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ObjectiveFacing challenges to establish a new national syndromicsurveillance system in the Netherlands for infectious diseases amongasylum seekers.IntroductionMost European countries are facing a continuous increased influxof asylum seekers [1]. Poor living conditions in crowded shelters andrefugee camps increase the risk for - outbreaks of - infectious diseasesin this vulnerable population. In line with ECDC recommendations[2], we aim to improve information on infectious diseases amongasylum seekers by establishing a new syndromic surveillance systemin the Netherlands. This system will complement the notifiabledisease system for infectious diseases.The aim of the syndromic surveillance system is to improve thedetecting of outbreaks of infectious diseases in asylum seekers’centres in an early stage of development to be able to take adequateand timely measures to prevent further spread, and to collectinformation on the burden of infection within this population.MethodsPrimary health care for asylum seekers in the Netherlands isorganized nationally by the Asylum Seekers Health Centre, withgeneral practitioners providing care in each reception centre. Generalpractitioners (GPs) act as gatekeepers for specialized, secondaryhealth care and the GP is the first professional to consult for healthproblems. Therefore, electronic health records (EHR) kept by GPsprovide a complete picture of this population. These EHRs containdata on diagnoses/symptoms and treatment of asylum seekers, usingthe International Classification of Primary Care (ICPC). This data isrecorded routinely, as part of the health care process. During summer2016, about 30,000 asylum seekers were housed in about 60 receptioncentres across the Netherlands.ResultsThe governance structure was layed down in a collaborationagreement between the Asylum Seekers Health Centre, the nationalinstitute of public health RIVM and NIVEL. To ensure privacy ofthe asylum seekers, a privacy protocol has been drawn, taking intoaccount strict privacy regulations in the Netherlands. The informationsystem provider of the health care centre developed an extraction toolthat automatically generates weekly data extracts from the electronichealth records system to a Trusted Third Party (TTP). Beforetransferring the data to NIVEL, the TTP removes directly identifyingpatient information, indirectly identifying information like date ofbirth is replaced by quarter and year, and the personal identificationnumber is replaced by a pseudonym. At NIVEL, all data is storedin a relational database, from which weekly research extracts aregenerated for infectious disease surveillance at RIVM after applyinga second pseudonymisation step (two-way pseudonimisation) [3].First data extracts are being expected mid-October 2016, after whichdata quality will be evaluated. Weekly, or daily, consultations rateswill be calculated based on the number of cases meeting predefineddefinitions, stratified by immigration centre, age group, sex andnationality. Numerators will be based on the number of populationhoused in the immigration centres.ConclusionsWith the cooperation of a national health care centre, providingprimary care to asylum seekers housed at several locations, and theinformation system provider of the health care centre, EHRs can beused for syndromic surveillance, taking into account strict privacyregulations. The new surveillance system will be evaluated after oneyear, focusing on data quality, usefulness, and the added value aboveto the notification of diseases.

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