Date

2022-1-28

Author

Şimşek Yılgın, Ezelsu

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Collaboration and data sharing are essential aspects of health research. Nevertheless, the number of sensitive health data breaches is increasing and there is a significant need to ensure that the privacy of patients is preserved. Health data accumulated in different repositories can be useful for statistical analysis, data mining and machine learning tasks; which results in long-term value for both healthcare professionals and patients. Preserving the privacy and ensuring the security is essential while coping with the distributed nature of health data during clinical research. Data de-identification and anonymization techniques are highly beneficial for protecting patients data against privacy risks. In this thesis, de-identification and anonymization of health data existing in HL7 FHIR repositories has been studied to ensure the privacy protection. This work presents the development of Data Privacy Tool, which includes a novel technique for de-identification of HL7 FHIR data, as well as provides a graphical user interface. The study also includes assessment of the outcomes from a privacy point of view comparing various de-identification techniques. This study has examined the existing algorithms for de-identification of health data and proposed an efficient methodology to develop the privacy preservation layer. The results of this study are analyzed through several experiments. This study aims to contribute to a research project called FAIR4Health under the scope of the Horizon 2020 Research and Innovation Programme.

Subject Keywords

Health data, FHIR, De-identification, K-anonymity, L-diversity

URI

https://hdl.handle.net/11511/95962

Collections

Graduate School of Natural and Applied Sciences, Thesis