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.