Regulations in various countries permit the reuse of health information without patient authorization provided the data is "de-identified". In the United States, for instance, the Privacy Rule of the Health Insurance Portability and Accountability Act defines two distinct approaches to achieve de-identification; the first is Safe Harbor, which requires the removal of a list of identifiers and the second is Expert Determination, which requires that an expert certify the re-identification risk inherent in the data is sufficiently low. In reality, most healthcare organizations eschew the expert route because there are no standardized approaches and Safe Harbor is much simpler to interpret. This, however, precludes a wide range of worthwhile endeavors that are dependent on features suppressed by Safe Harbor, such as gerontological studies requiring detailed ages over 89. In response, we propose a novel approach to automatically discover alternative de-identification policies that contain no more re-identification risk than Safe Harbor. We model this task as a lattice-search problem, introduce a measure to capture the re-identification risk, and develop an algorithm that efficiently discovers polices by exploring the lattice. Using a cohort of approximately 3000 patient records from the Vanderbilt University Medical Center, as well as the Adult dataset from the UCI Machine Learning Repository, we also experimentally verify that a large number of alternative policies can be discovered in an efficient manner.
Keywords: De-identification; Privacy; Safe Harbor.