An integrated framework for de-identifying unstructured medical data

While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any information that can be used to identify a patient. A considerable amount of research in data privacy community has been devoted to formalizing the notion of identifiability and developing techniques for anonymization but are focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in medical informatics community rely on simple identifier removal or grouping techniques without taking advantage of the research developments in the data privacy community. This paper attempts to fill the above gaps and presents a framework and prototype system for de-identifying health information including both structured and unstructured data. We empirically study a simple Bayesian classifier, a Bayesian classifier with a sampling based technique, and a conditional random field based classifier for extracting identifying attributes from unstructured data. We deploy a k-anonymization based technique for de-identifying the extracted data to preserve maximum data utility. We present a set of preliminary evaluations showing the effectiveness of our approach.