title: A data recipient centered de-identification method to retain statistical attributes.
abstract: Privacy has always been a great concern of patients and medical service providers. As a result of the recent advances in information technology and the government's push for the use of Electronic Health Record (EHR) systems, a large amount of medical data is collected and stored electronically. This data needs to be made available for analysis but at the same time patient privacy has to be protected through de-identification. Although biomedical researchers often describe their research plans when they request anonymized data, most existing anonymization methods do not use this information when de-identifying the data. As a result, the anonymized data may not be useful for the planned research project. This paper proposes a data recipient centered approach to tailor the de-identification method based on input from the recipient of the data. We demonstrate our approach through an anonymization project for biomedical researchers with specific goals to improve the utility of the anonymized data for statistical models used for their research project. The selected algorithm improves a privacy protection method called Condensation by Aggarwal et al. Our methods were tested and validated on real cancer surveillance data provided by the Kentucky Cancer Registry.
authors.affiliation: University of Kentucky, 2365 Harrodsburg Rd., Suite A230, Lexington, KY 40504, USA. Electronic address: tamas.gal@uky.edu.
authors.forename: Tamas S
authors.initials: TS
authors.lastname: Gal
authors.affiliation: University of Kentucky, 2365 Harrodsburg Rd., Suite A230, Lexington, KY 40504, USA. Electronic address: tct@kcr.uky.edu.
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Tucker
authors.affiliation: University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. Electronic address: gangopad@umbc.edu.
authors.forename: Aryya
authors.initials: A
authors.lastname: Gangopadhyay
authors.affiliation: University of Maryland at Baltimore County, 1000 Hilltop Circle, Baltimore, MD 21250, USA. Electronic address: zhchen@umbc.edu.
authors.forename: Zhiyuan
authors.initials: Z
authors.lastname: Chen
mesh_terms: D057286:Electronic Health Records; D010355:Patient Identification Systems; D018907:Privacy
keywords: Privacy; Statistical analysis; Utility based privacy preserving data mining