Privacy-preserving publication of diagnosis codes for effective biomedical analysis

Patient-specific records contained in Electronic Medical Record (EMR) systems are increasingly combined with genomic sequences and deposited into bio-repositories. This allows researchers to perform large-scale, low-cost biomedical studies, such as Genome-Wide Association Studies (GWAS) aimed at identifying associations between genetic factors and complex health-related phenomena, which are an integral facet of personalized medicine. Disseminating this data, however, raises serious privacy concerns because patients´ genomic sequences can be linked to their identities through diagnosis codes. This work proposes an approach that guards against this type of data linkage by modifying diagnosis codes in a way that limits the probability of associating a patient´s identity to their genomic sequence. Experiments using EMRs from the Vanderbilt University Medical Center verify that our approach generates data that can support up to 29:4% more GWAS than the best-so-far method, while permitting biomedical analysis tasks several orders of magnitude more accurately.