IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/35463.html
   My bibliography  Save this paper

Avoiding disclosure of individually identifiable health information: a literature review

Author

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

Abstract

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.

Suggested Citation

  • Prada, Sergio I & Gonzalez, Claudia & Borton, Joshua & Fernandes-Huessy, Johannes & Holden, Craig & Hair, Elizabeth & Mulcahy, Tim, 2011. "Avoiding disclosure of individually identifiable health information: a literature review," MPRA Paper 35463, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:35463
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/35463/1/MPRA_paper_35463.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Julia Lane & Claudia Schur, 2009. "Balancing Access to Data And Privacy. A review of the issues and approaches for the future," RatSWD Working Papers 113, German Data Forum (RatSWD).
    3. 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.
    4. 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.
    5. 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.
    6. Skinner, Chris J. & Shlomo, Natalie, 2008. "Assessing identification risk in survey microdata using log-linear models," LSE Research Online Documents on Economics 39112, London School of Economics and Political Science, LSE Library.
    7. Skinner, Chris J., 2007. "The probability of identification: applying ideas from forensic statistics to disclosure risk assessment," LSE Research Online Documents on Economics 39105, London School of Economics and Political Science, LSE Library.
    8. 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, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Erdem, Erkan & Prada, Sergio I, 2011. "Creation of public use files: lessons learned from the comparative effectiveness research public use files data pilot project," MPRA Paper 35478, University Library of Munich, Germany.
    2. Task Force Members Include: Lilli Japec & Frauke Kreuter & Marcus Berg & Paul Biemer & Paul Decker & Cliff Lampe & Julia Lane & Cathy O'Neil & Abe Usher, "undated". "AAPOR Report on Big Data," Mathematica Policy Research Reports 4eb9b798fd5b42a8b53a9249c, Mathematica Policy Research.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sergio I. Prada & Claudia González-Martínez & Joshua Borton & Johannes Fernandes-Huessy & Craig Holden & Elizabeth Hair & and Tim Mulcahy, 2011. "Avoiding Disclosure of Individually Identifiable Health Information," SAGE Open, , vol. 1(3), pages 21582440114, October.
    2. James Jackson & Robin Mitra & Brian Francis & Iain Dove, 2022. "Using saturated count models for user‐friendly synthesis of large confidential administrative databases," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1613-1643, October.
    3. Christine M. O'Keefe & James O. Chipperfield, 2013. "A Summary of Attack Methods and Confidentiality Protection Measures for Fully Automated Remote Analysis Systems," International Statistical Review, International Statistical Institute, vol. 81(3), pages 426-455, December.
    4. Cinzia Carota & Maurizio Filippone & Silvia Polettini, 2022. "Assessing Bayesian Semi‐Parametric Log‐Linear Models: An Application to Disclosure Risk Estimation," International Statistical Review, International Statistical Institute, vol. 90(1), pages 165-183, April.
    5. Shlomo, Natalie & Skinner, Chris, 2022. "Measuring risk of re-identification in microdata: state-of-the art and new directions," LSE Research Online Documents on Economics 117168, London School of Economics and Political Science, LSE Library.
    6. Drechsler, Jörg & Reiter, Jerome P., 2011. "An empirical evaluation of easily implemented, nonparametric methods for generating synthetic datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(12), pages 3232-3243, December.
    7. Eurosystem Household Finance and Consumption Network, 2013. "The Eurosystem Household Finance and Consumption Survey - Methodological report," Statistics Paper Series 1, European Central Bank.
    8. Li‐Chun Zhang & Gustav Haraldsen, 2022. "Secure big data collection and processing: Framework, means and opportunities," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1541-1559, October.
    9. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 50(1 (Spring), pages 221-293.
    10. Krenzke Tom & Li Jianzhu & Gentleman Jane F. & Moriarity Chris, 2013. "Addressing Disclosure Concerns and Analysis Demands in a Real-Time Online Analytic System," Journal of Official Statistics, Sciendo, vol. 29(1), pages 99-124, March.
    11. Iwona Bąk & Katarzyna Cheba, 2022. "Green Transformation: Applying Statistical Data Analysis to a Systematic Literature Review," Energies, MDPI, vol. 16(1), pages 1-22, December.
    12. Favaro, Stefano & Panero, Francesca & Rigon, Tommaso, 2021. "Bayesian nonparametric disclosure risk assessment," LSE Research Online Documents on Economics 117305, London School of Economics and Political Science, LSE Library.
    13. John M. Abowd & Ian M. Schmutte, 2015. "Economic Analysis and Statistical Disclosure Limitation," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 46(1 (Spring), pages 221-293.
    14. Chipperfield James O., 2014. "Disclosure-Protected Inference with Linked Microdata Using a Remote Analysis Server," Journal of Official Statistics, Sciendo, vol. 30(1), pages 123-146, March.
    15. Sudipto Banerjee & David Blau, 2016. "Employment Trends by Age in the United States: Why Are Older Workers Different?," Journal of Human Resources, University of Wisconsin Press, vol. 51(1), pages 163-199.
    16. Bhalotra, Sonia R. & Venkataramani, Atheendar, 2011. "The Captain of the Men of Death and His Shadow: Long-Run Impacts of Early Life Pneumonia Exposure," IZA Discussion Papers 6041, Institute of Labor Economics (IZA).
    17. Raj Chetty & John N. Friedman, 2019. "A Practical Method to Reduce Privacy Loss When Disclosing Statistics Based on Small Samples," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 414-420, May.
    18. Huang, Yunyou & Zhan, Jianfeng & Luo, Chunjie & Wang, Lei & Wang, Nana & Zheng, Daoyi & Fan, Fanda & Ren, Rui, 2019. "An electricity consumption model for synthesizing scalable electricity load curves," Energy, Elsevier, vol. 169(C), pages 674-683.
    19. Dr. Jörg Höhne & Julia Höninger, 2012. "Morpheus – Remote access to micro data with a quality measure," RatSWD Working Papers 203, German Data Forum (RatSWD).
    20. Edoardo Ciscato & Alfred Galichon & Marion Goussé, 2020. "Like Attract Like? A Structural Comparison of Homogamy across Same-Sex and Different-Sex Households," Journal of Political Economy, University of Chicago Press, vol. 128(2), pages 740-781.

    More about this item

    Keywords

    public use files; disclosure avoidance; reidentification; de-identification; data utility;
    All these keywords.

    JEL classification:

    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:35463. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.