HIDE: heterogeneous information DE-identification
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Masking Gateway for Enterprises
Languages: From Formal to Natural
Formal anonymity models for efficient privacy-preserving joins
Data & Knowledge Engineering
An evaluation of feature sets and sampling techniques for de-identification of medical records
Proceedings of the 1st ACM International Health Informatics Symposium
An application architecture to facilitate multi-site clinical trial collaboration in the cloud
Proceedings of the 2nd International Workshop on Software Engineering for Cloud Computing
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While there is an increasing need to share medical information for public health research, such data sharing must preserve patient privacy without disclosing any identifiable information. 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 prototype system for de-identifying health information including both structured and unstructured data. It deploys a conditional random fields based technique for extracting identifying attributes from unstructured data and k-anonymization based technique for de-identifying the data while preserving maximum data utility. We present a set of preliminary evaluations showing the effectiveness of our approach.