HIDE: Heterogeneous Information DE-identification
Authors
- James Gardner (Emory University, USA)
- Li Xiong (Emory University, USA)
- Kanwei Li (Emory University, USA)
- James J. Lu (Emory University, USA)
Abstract
While there is an increasing need to share data that may contain personal information, such data sharing must preserve individual privacy without disclosing any identifiable information. A considerable amount of research in the data privacy community has been devoted to formalizing the notion of identifiability with many techniques for anonymization, but is focused exclusively on structured data. On the other hand, efforts on de-identifying medical text documents in the medical informatics community are highly specialized for specific document types or a subset of identifiers. In addition, they rely on simple identifier removal or grouping techniques and do not take advantage of the research developments in the data privacy community. We developed an integrated system, HIDE, for Heterogeneous Information DE-identification including structured and unstructured data utilizing existing anonymization techniques. We demonstrate a prototype of our system and show the effectiveness of our approach through a set of real data augmented with synthesized data.
Session
EDBT Demo Session 1: Demo Group 1 (Tuesday, March 24, 14:00—17:30)