IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v53y2009i4p1475-1482.html
   My bibliography  Save this article

Verification servers: Enabling analysts to assess the quality of inferences from public use data

Author

Listed:
  • Reiter, Jerome P.
  • Oganian, Anna
  • Karr, Alan F.

Abstract

To protect confidentiality, statistical agencies typically alter data before releasing them to the public. Ideally, although generally not done, the agency also provides a way for secondary data analysts to assess the quality of inferences obtained with the released data. Quality measures can help secondary data analysts to identify inaccurate conclusions resulting from the disclosure limitation procedures, as well as have confidence in accurate conclusions. We propose a framework for an interactive, web-based system that analysts can query for measures of inferential quality. As we illustrate, agencies seeking to build such systems must consider the additional disclosure risks from releasing quality measures. We suggest some avenues of research on limiting these risks.

Suggested Citation

  • Reiter, Jerome P. & Oganian, Anna & Karr, Alan F., 2009. "Verification servers: Enabling analysts to assess the quality of inferences from public use data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1475-1482, February.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:4:p:1475-1482
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(08)00475-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jerome P. Reiter, 2005. "Releasing multiply imputed, synthetic public use microdata: an illustration and empirical study," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 185-205, January.
    2. Karr, A.F. & Kohnen, C.N. & Oganian, A. & Reiter, J.P. & Sanil, A.P., 2006. "A Framework for Evaluating the Utility of Data Altered to Protect Confidentiality," The American Statistician, American Statistical Association, vol. 60, pages 224-232, August.
    3. John M. Abowd & Simon D. Woodcock, 2004. "Multiply-Imputing Confidential Characteristics and File Links in Longitudinal Linked Data," Longitudinal Employer-Household Dynamics Technical Papers 2004-04, Center for Economic Studies, U.S. Census Bureau.
    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. Joshua Snoke & Gillian M. Raab & Beata Nowok & Chris Dibben & Aleksandra Slavkovic, 2018. "General and specific utility measures for synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 663-688, June.
    2. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    3. Jerome P. Reiter, 2009. "Using Multiple Imputation to Integrate and Disseminate Confidential Microdata," International Statistical Review, International Statistical Institute, vol. 77(2), pages 179-195, August.
    4. Dr. Jörg Höhne & Julia Höninger, 2012. "Morpheus – Remote access to micro data with a quality measure," RatSWD Working Papers 203, German Data Forum (RatSWD).
    5. John M. Abowd & Ian M. Schmutte & Lars Vilhuber, 2018. "Disclosure Limitation and Confidentiality Protection in Linked Data," Working Papers 18-07, Center for Economic Studies, U.S. Census Bureau.

    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. 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.
    2. Joseph W. Sakshaug & Trivellore E. Raghunathan, 2014. "Generating synthetic microdata to estimate small area statistics in the American Community Survey," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(3), pages 341-368, June.
    3. Reiter, Jerome P., 2008. "Selecting the number of imputed datasets when using multiple imputation for missing data and disclosure limitation," Statistics & Probability Letters, Elsevier, vol. 78(1), pages 15-20, January.
    4. Joshua Snoke & Gillian M. Raab & Beata Nowok & Chris Dibben & Aleksandra Slavkovic, 2018. "General and specific utility measures for synthetic data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 663-688, June.
    5. Loong Bronwyn & Rubin Donald B., 2017. "Multiply-Imputed Synthetic Data: Advice to the Imputer," Journal of Official Statistics, Sciendo, vol. 33(4), pages 1005-1019, December.
    6. Satkartar K. Kinney & Jerome P. Reiter & Javier Miranda, 2014. "Improving The Synthetic Longitudinal Business Database," Working Papers 14-12, Center for Economic Studies, U.S. Census Bureau.
    7. Klein Martin & Sinha Bimal, 2013. "Statistical Analysis of Noise-Multiplied Data Using Multiple Imputation," Journal of Official Statistics, Sciendo, vol. 29(3), pages 425-465, June.
    8. Claire McKay Bowen & Fang Liu & Bingyue Su, 2021. "Differentially private data release via statistical election to partition sequentially," METRON, Springer;Sapienza Università di Roma, vol. 79(1), pages 1-31, April.
    9. Templ Matthias, 2015. "Quality Indicators for Statistical Disclosure Methods: A Case Study on the Structure of Earnings Survey," Journal of Official Statistics, Sciendo, vol. 31(4), pages 737-761, December.
    10. Ainara González de San Román & Yolanda F. Rebollo‐Sanz, 2018. "An Estimation Of Worker And Firm Effects With Censored Data," Bulletin of Economic Research, Wiley Blackwell, vol. 70(4), pages 459-482, October.
    11. Woodcock, Simon D. & Benedetto, Gary, 2009. "Distribution-preserving statistical disclosure limitation," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4228-4242, October.
    12. Tatiana Komarova & Denis Nekipelov & Evgeny Yakovlev, 2018. "Identification, data combination, and the risk of disclosure," Quantitative Economics, Econometric Society, vol. 9(1), pages 395-440, March.
    13. Dong Hua & Meeden Glen, 2016. "Constructing Synthetic Samples," Journal of Official Statistics, Sciendo, vol. 32(1), pages 113-127, March.
    14. Martin Klein & Ricardo Moura & Bimal Sinha, 2021. "Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 273-287, May.
    15. 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.
    16. repec:crs:wpidms:m2016-07 is not listed on IDEAS
    17. Andrés F. Barrientos & Alexander Bolton & Tom Balmat & Jerome P. Reiter & John M. de Figueiredo & Ashwin Machanavajjhala & Yan Chen & Charles Kneifel & Mark DeLong, 2017. "A Framework for Sharing Confidential Research Data, Applied to Investigating Differential Pay by Race in the U. S. Government," NBER Working Papers 23534, National Bureau of Economic Research, Inc.
    18. Yi Qian & Hui Xie, 2013. "Drive More Effective Data-Based Innovations: Enhancing the Utility of Secure Databases," NBER Working Papers 19586, National Bureau of Economic Research, Inc.
    19. Soumya Mukherjee & Aratrika Mustafi & Aleksandra Slavkovi'c & Lars Vilhuber, 2023. "Assessing Utility of Differential Privacy for RCTs," Papers 2309.14581, arXiv.org.
    20. Sonya Vartivarian & John L. Czajka & Michael Weber, "undated". "Measuring Disclosure Risk and an Examination of the Possibilities of Using Synthetic Data in the Individual Income Tax Return Public Use File," Mathematica Policy Research Reports ab85aed60a3e429786cfcbfdc, Mathematica Policy Research.
    21. Jahangir Alam M. & Dostie Benoit & Drechsler Jörg & Vilhuber Lars, 2020. "Applying data synthesis for longitudinal business data across three countries," Statistics in Transition New Series, Polish Statistical Association, vol. 21(4), pages 212-236, August.

    More about this item

    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:eee:csdana:v:53:y:2009:i:4:p:1475-1482. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.