The disclosure of diagnosis codes can breach research participants' privacy.

Objective: De-identified clinical data in standardized form (eg, diagnosis codes), derived from electronic medical records, are increasingly combined with research data (eg, DNA sequences) and disseminated to enable scientific investigations. This study examines whether released data can be linked with identified clinical records that are accessible via various resources to jeopardize patients' anonymity, and the ability of popular privacy protection methodologies to prevent such an attack.

Design: The study experimentally evaluates the re-identification risk of a de-identified sample of Vanderbilt's patient records involved in a genome-wide association study. It also measures the level of protection from re-identification, and data utility, provided by suppression and generalization.

Measurement: Privacy protection is quantified using the probability of re-identifying a patient in a larger population through diagnosis codes. Data utility is measured at a dataset level, using the percentage of retained information, as well as its description, and at a patient level, using two metrics based on the difference between the distribution of Internal Classification of Disease (ICD) version 9 codes before and after applying privacy protection.

Results: More than 96% of 2800 patients' records are shown to be uniquely identified by their diagnosis codes with respect to a population of 1.2 million patients. Generalization is shown to reduce further the percentage of de-identified records by less than 2%, and over 99% of the three-digit ICD-9 codes need to be suppressed to prevent re-identification.

Conclusions: Popular privacy protection methods are inadequate to deliver a sufficiently protected and useful result when sharing data derived from complex clinical systems. The development of alternative privacy protection models is thus required.