Quantifying and Mitigating Privacy Risks in Biomedical Data

The decreasing costs of molecular profiling have fueled the biomedical research community with a plethora of new types of biomedical data, allowing for a breakthrough towards a more precise and personalized medicine. However, the release of these intrinsically highly sensitive, interdependent data poses a new severe privacy threat. So far, the security community has mostly focused on privacy risks arising from genomic data. However, the manifold privacy risks stemming from other types of biomedical data – and epigenetic data in particular – have been largely overlooked. In this thesis, we provide means to quantify and protect the privacy of individuals’ biomedical data. Besides the genome, we specifically focus on two of the most important epigenetic elements influencing human health: microRNAs and DNA methylation. We quantify the privacy for multiple realistic attack scenarios, namely, (1) linkability attacks along the temporal dimension, between different types of data, and between related individuals, (2) membership attacks, and (3) inference attacks. Our results underline that the privacy risks inherent to biomedical data have to be taken seriously. Moreover, we present and evaluate solutions to preserve the privacy of individuals. Our mitigation techniques stretch from the differentially private release of epigenetic data, considering its utility, up to cryptographic constructions to securely, and privately evaluate a random forest on a patient’s data.