Privacy-Preserving Access Control in Electronic Health Record Linkage

Sharing aggregated electronic health records (EHRs) for integrated health care and public health studies is increasingly demanded. Patient privacy demands that anonymisation procedures are in place for data sharing. However traditional methods such as k-anonymity and its derivations are often over-generalizing resulting in lower data accuracy. To tackle this issue, we present the Semantic Linkage K-Anonymity (SLKA) approach supporting ongoing record linkages. We show how SLKA balances privacy and utility preservation through detecting risky combinations hidden in data releases

Implementation and evaluation of microaggregation algorithms for categorical data

Different data anonymization algorithms have been proposed in the literature, but sometimes it is not easy for the practitioners to understand which one is better for different situations.In a growingly digitalised world, the need for data privacy is apparent. Data scientists have contributed much previous work to ensure privacy regarding numerical data attributes in published datasets. However, work with categorical data tends to significantly affect the data utility concerning information loss, and less feasible research is available. The thesis aims to describe, implement and compare multiple microaggregation algorithms for categorical data. To achieve the goals of the thesis, and provide valuable output, multiple new proposals to handle categorical data based on the Mondrian algorithm were presented as part of the thesis. It was found that the proposals fared well compared to some previously presented algorithms, both in terms of algorithm execution time, potential information loss and reidentification risk