Готові списки джерел за темами / Pseudonymisation

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Добірка наукової літератури з теми "Pseudonymisation"

Автор: Grafiati

Опубліковано: 10 січня 2023

Оновлено: 28 січня 2023

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Статті в журналах з теми "Pseudonymisation"

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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Анотація:

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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Анотація:

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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Анотація:

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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Більше джерел

Дисертації з теми "Pseudonymisation"

Vimalachandran, Pasupathy. "Privacy and Security of Storing Patients’ Data in the Cloud." Thesis, 2019. https://vuir.vu.edu.au/40598/.

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Анотація:

A better health care service must ensure patients receive the right care, in the right place, at the right time. In enabling better health care, the impact of technology is immense. Technological breakthroughs are revolutionising the way health care is being delivered. To deliver better health care, sharing health information amongst health care providers who are involved with the care is critical. An Electronic Health Record (EHR) platform is used to share the health information among those health care providers faster, as a result of technological advancement including the Internet and the Cloud. However, when integrating such technologies to support the provision of health care, they lead to major concerns over privacy and security of health sensitive information. The privacy and security concerns include a wide range of ethical and legal issues associated with the system. These concerns need to be considered and addressed for the implementation of EHR systems. In a shared environment like EHRs, these concerns become more significant. In this thesis, the author explores and discusses the situations where these concerns do arise in a health care environment. This thesis also covers different attacks that have targeted health care information in the past, with potential solutions for every attack identified. From these findings, the proposed system is designed and developed to provide considerable security assurance for a health care organisation when using the EHR systems. Furthermore, the My Health Record (MyHR) system is introduced in Australia to allow an individual’s doctors and other health care providers to access the individual’s health information. Privacy and security in using MyHR is a major challenge that impacts its usage. Taking all these concerns into account, the author will also focus on discussing and analysing major existing access control methods, various threats for data privacy and security concerns over EHR use and the importance of data integrity while using MyHR or any other EHR systems. To preserve data privacy and security and prevent unauthorised access to the system, the author proposes a three-tier security model. In this three-tier security model, the first tier covers an access control mechanism, an Intermediate State of Databases (ISD) is included in the second tier and the third layer involves cryptography/data encryption and decryption. These three tiers, collectively, cover different forms of attacks from different sources including unauthorised access from inside a health care organisation. In every tier, a specific technique has been utilised. In tier one, an Improved Access Control Mechanism (IACM) known as log-in pair, pseudonymisation technique is proposed in tier two and a special new encryption and decryption algorithm has been developed and used for tier three in the proposed system. In addition, the design, development, and implementation of the proposed model have been described to enable and evaluate the operational protocol. Problem 1. Non-clinical staff including reception, admin staff access sensitive health clinical information (insiders). Solution 1. An improved access control mechanism named log-in pair is introduced and occupied in tier one. Problem 2. Researchers and research institutes access health data sets for research activities (outsiders). Solution 2. Pseudonymisation technique, in tier two, provides de-identified required data with relationships, not the sensitive data. Problem 3. The massive amount of sensitive health data stored with the EHR system in the Cloud becomes more vulnerable to data attacks. Solution 3. A new encryption and decryption algorithm is achieved and used in tier three to provide high security while storing the data in the Cloud.

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Частини книг з теми "Pseudonymisation"

Murphy, Maria Helen. "Pseudonymisation and the smart city." In Creating Smart Cities, 182–93. Abingdon, Oxon ; New York, NY : Routledge, 2019. |Series: Regions and cities ; volume 131: Routledge, 2018. http://dx.doi.org/10.4324/9781351182409-14.

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Veeningen, Meilof, Benne de Weger, and Nicola Zannone. "Formal Modelling of (De)Pseudonymisation: A Case Study in Health Care Privacy." In Security and Trust Management, 145–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38004-4_10.

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Čtvrtník, Mikuláš. "Data Minimisation—Storage Limitation—Archiving." In Archives and Records, 197–240. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18667-7_8.

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Анотація:

AbstractThe chapter summarises the conclusions drawn for the area of data reduction and minimisation in records management and archiving. A major focus is placed, among other things, on the process of archival appraisal, within which the most significant data and record reduction in records management and archiving is carried out. Archival science, methodology, and practice, however, have so far neglected the potential risks of the misuse of sensitive personal data contained in permanently (or long-term) stored records and have focused almost exclusively on the records information content and their future usability by various research projects and private research interests. The sector should aspire to change this in the future and include a more substantial consideration of the protection of (not only) personality rights and privacy in archival appraisal. In this chapter, the author thus analyses some models of records appraisal as they have taken shape in the post-1945 period. In the analysis of the process of the reduction and minimisation of data maintained in archives, the author will also look at the anonymisation and pseudonymisation of data that are either already stored or aspire to be archived in the future. In this context, it will finally also address the current growing risks of deanonymisation and reidentification.

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Tosoni, Luca. "Article 4(5). Pseudonymisation." In The EU General Data Protection Regulation (GDPR). Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198826491.003.0011.

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Анотація:

Article 6(4)(e) (Compatibility of processing purposes); Article 9(2)(j) (Processing of special categories of personal data) (see also recital 75); Article 25(1) (Data protection by design and by default) (see also recital 78); Article 32(1)(a) (Security of processing); Article 33(1) (Notification of personal data breach to supervisory authority) (see also recital 85); Article 40(2)(d) (Codes of conduct); Article 89(1) (Safeguards and derogations relating to processing for archiving purposes, scientific or historical research purposes or statistical purposes).

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Baumgartner, Martin, Günter Schreier, Dieter Hayn, Karl Kreiner, Lukas Haider, Fabian Wiesmüller, Luca Brunelli, and Gerhard Pölzl. "Impact Analysis of De-Identification in Clinical Notes Classification." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220368.

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Анотація:

Background: Clinical notes provide valuable data in telemonitoring systems for disease management. Such data must be converted into structured information to be effective in automated analysis. One way to achieve this is by classification (e.g. into categories). However, to conform with privacy regulations and concerns, text is usually de-identified. Objectives: This study investigated the effects of de-identification on classification. Methods: Two pseudonymisation and two classification algorithms were applied to clinical messages from a telehealth system. Divergence in classification compared to clear text classification was measured. Results: Overall, de-identification notably altered classification. The delicate classification algorithm was severely impacted, especially losses of sensitivity were noticeable. However, the simpler classification method was more robust and in combination with a more yielding pseudonymisation technique, had only a negligible impact on classification. Conclusion: The results indicate that de-identification can impact text classification and suggest, that considering de-identification during development of the classification methods could be beneficial.

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Tosoni, Luca. "Article 4(12). Personal data breach." In The EU General Data Protection Regulation (GDPR). Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198826491.003.0018.

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Анотація:

A personal data breach may, if not addressed in an appropriate and timely manner, result in physical, material or non-material damage to natural persons such as loss of control over their personal data or limitation of their rights, discrimination, identity theft or fraud, financial loss, unauthorised reversal of pseudonymisation, damage to reputation, loss of confidentiality of personal data protected by professional secrecy or any other significant economic or social disadvantage to the natural person concerned.

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Bygrave, Lee A., and Luca Tosoni. "Article 4(1). Personal data." In The EU General Data Protection Regulation (GDPR). Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198826491.003.0007.

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Анотація:

The principles of data protection should apply to any information concerning an identified or identifiable natural person. Personal data which have undergone pseudonymisation, which could be attributed to a natural person by the use of additional information should be considered to be information on an identifiable natural person. To determine whether a natural person is identifiable, account should be taken of all the means reasonably likely to be used, such as singling out, either by the controller or by another person to identify the natural person directly or indirectly.

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Bygrave, Lee A. "Article 25 Data protection by design and by default." In The EU General Data Protection Regulation (GDPR). Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198826491.003.0060.

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Анотація:

Article 4(5) (Definition of ‘pseudonymisation’) (see too recital 28); Article 5(2) (Accountability) (see too recital 11); Article 6(4)(e) (Compatibility); Article 22 (Automated individual decision-making, including profiling) (see too recital 71); Article 24 (Responsibility of controllers); Article 28 (Processors) (see too recital 81); Article 32 (Security of processing) (see too recital 83); Article 34(3)(a) (Communication of personal data breach to data subject) (see too recitals 87–88); Article 35 (Data protection impact assessment) (see too recital 84); Article 40 (Codes of conduct); Article 83(2)(d) and 83(4) (Fines); Article 89(1) (Safeguards relating to processing of personal data for archiving purposes in the public interest, scientific or historical research purposes or statistical purposes).

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Тези доповідей конференцій з теми "Pseudonymisation"

Noé, Paul-Gauthier, Jean-François Bonastre, Driss Matrouf, N. Tomashenko, Andreas Nautsch, and Nicholas Evans. "Speech Pseudonymisation Assessment Using Voice Similarity Matrices." In Interspeech 2020. ISCA: ISCA, 2020. http://dx.doi.org/10.21437/interspeech.2020-2720.

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Noumeir, Rita, Alain Lemay, and Jean-Marc Lina. "Pseudonymisation of radiology data for research purposes." In Medical Imaging, edited by Osman M. Ratib and Steven C. Horii. SPIE, 2005. http://dx.doi.org/10.1117/12.594696.

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Kayem, Anne, Nikolai Podlesny, Christoph Meinel, and Anja Lehmann. "On Chameleon Pseudonymisation and Attribute Compartmentation-as-a-Service." In 18th International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010552207040714.

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Limba, Tadas, and Aurimas Šidlauskas. "PERSONAL DATA PROCESSING IN EDUCATIONAL INSTITUTIONS: ANONYMISATION AND PSEUDONYMISATION." In 13th annual International Conference of Education, Research and Innovation. IATED, 2020. http://dx.doi.org/10.21125/iceri.2020.2262.

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Kayem, Anne, Nikolai Podlesny, and Christoph Meinel. "On Chameleon Pseudonymisation and Attribute Compartmentation-as-a-Service." In 18th International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010552200002998.

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Pedrosa, Micael, Andre Zuquete, and Carlos Costa. "Pseudonymisation with Break-the-Glass Compatibility for Health Records in Federated Services." In 2019 IEEE 19th International Conference on Bioinformatics and Bioengineering (BIBE). IEEE, 2019. http://dx.doi.org/10.1109/bibe.2019.00056.

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Blokland, Rogier, Niko Partanen, and Michael Rießler. "A pseudonymisation method for language documentation corpora: An experiment with spoken Komi." In Proceedings of the Sixth International Workshop on Computational Linguistics of Uralic Languages. Stroudsburg, PA, USA: Association for Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.iwclul-1.1.

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Tinabo, R., F. Mtenzi, and B. O'Shea. "Anonymisation vs. Pseudonymisation: Which one is most useful for both privacy protection and usefulness of e-healthcare data." In 2009 4th International Conference for Internet Technology and Secured Transactions (ICITST 2009). IEEE, 2009. http://dx.doi.org/10.1109/icitst.2009.5402501.

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