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Avoiding disclosure of individually identifiable health information: a literature review

Listed author(s):
  • Prada, Sergio I
  • Gonzalez, Claudia
  • Borton, Joshua
  • Fernandes-Huessy, Johannes
  • Holden, Craig
  • Hair, Elizabeth
  • Mulcahy, Tim

Achieving data and information dissemination without arming anyone is a central task of any entity in charge of collecting data. In this article, the authors examine the literature on data and statistical confidentiality. Rather than comparing the theoretical properties of specific methods, they emphasize the main themes that emerge from the ongoing discussion among scientists regarding how best to achieve the appropriate balance between data protection, data utility, and data dissemination. They cover the literature on de-identification and reidentification methods with emphasis on health care data. The authors also discuss the benefits and limitations for the most common access methods. Although there is abundant theoretical and empirical research, their review reveals lack of consensus on fundamental questions for empirical practice: How to assess disclosure risk, how to choose among disclosure methods, how to assess reidentification risk, and how to measure utility loss.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 35463.

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Date of creation: 14 Dec 2011
Publication status: Published in SAGE Open (2011): pp. 1-16
Handle: RePEc:pra:mprapa:35463
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  1. C. J. Skinner, 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(1), pages 195-212.
  2. Daniel Weinberg & John Abowd & Sandra Rowland & Philip Steel & Laura Zayatz, 2007. "Access Methods for United States Microdata," Working Papers 07-25, Center for Economic Studies, U.S. Census Bureau.
  3. Julia Lane & Claudia Schur, 2009. "Balancing Access to Data And Privacy. A review of the issues and approaches for the future," Working Paper Series of the German Council for Social and Economic Data 113, German Council for Social and Economic Data (RatSWD).
  4. John M. Abowd & Julia I. Lane, 2004. "New Approaches to Confidentiality Protection Synthetic Data, Remote Access and Research Data Centers," Longitudinal Employer-Household Dynamics Technical Papers 2004-03, Center for Economic Studies, U.S. Census Bureau.
  5. Skinner, Chris & Shlomo, Natalie, 2008. "Assessing Identification Risk in Survey Microdata Using Log-Linear Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(483), pages 989-1001.
  6. J. Trent Alexander & Michael Davern & Betsey Stevenson, 2010. "Inaccurate age and sex data in the Census PUMS files: Evidence and Implications," NBER Working Papers 15703, National Bureau of Economic Research, Inc.
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