Abstract
The availability of genomic data resulting from rapid developments in gene sequencing technologies has accelerated the progress in genome epidemiological studies, which also contributes to the realization of genomebased disease-associated risk prediction as part of a personalized medical treatment. However, privacy concerns currently hinder the ubiquitous exploita-tion of personal genomic and clinical data. Accordingly, it is necessary to develop privacy-preserving methods that will allow the analysis of genomic and clinical data for genetic epidemiology and personalized medication as below. We give a broad overview of diverse risks and threts attached to the dissemination or publication of genetic data. As major privacy-enhancing technologies to reduce privacy risks, secure multiparty computation and differencial privacy are then introduced. We describe how these technologies preserve the privacy of genomic data when applied to genetic epidemiology and personalized medication.
