IDEAS home Printed from https://ideas.repec.org/p/iab/iabdpa/200720.html
   My bibliography  Save this paper

Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality

Author

Listed:
  • Reiter, Jerome P.
  • Drechsler, Jörg

Abstract

"To protect the cofidentiality of survey respondents' identities and sensitive attributes, statistical agencies can release data in which cofidential values are replaced with multiple imputations. These are called synthetic data. We propose a two-stage approach to generating synthetic data that enables agencies to release different numbers of imputations for different variables. Generation in two stages can reduce computational burdens, decrease disclosure risk, and increase inferential accuracy relative to generation in one stage. We present methods for obtaining inferences from such data. We describe the application of two stage synthesis to creating a public use file for a German business database." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Reiter, Jerome P. & Drechsler, Jörg, 2007. "Releasing multiply-imputed synthetic data generated in two stages to protect confidentiality," IAB-Discussion Paper 200720, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabdpa:200720
    as

    Download full text from publisher

    File URL: https://doku.iab.de/discussionpapers/2007/dp2007.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Donald B. Rubin, 2003. "Nested multiple imputation of NMES via partially incompatible MCMC," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 57(1), pages 3-18, February.
    2. Drechsler, Jörg & Dundler, Agnes & Bender, Stefan & Rässler, Susanne & Zwick, Thomas, 2007. "A new approach for disclosure control in the IAB Establishment Panel : multiple imputation for a better data access," IAB-Discussion Paper 200711, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    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. C. J. Skinner & M. J. Elliot, 2002. "A measure of disclosure risk for microdata," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 855-867, October.
    5. 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.
    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. Jörg Höhne, 2008. "Anonymisierungsverfahren für Paneldaten," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 2(3), pages 259-275, October.
    2. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    3. Razzak Humera & Heumann Christian, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Statistics Poland, vol. 20(4), pages 33-58, December.
    4. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.

    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. 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.
    2. 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.
    3. 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.
    4. Jörg Drechsler, 2015. "Multiple Imputation of Multilevel Missing Data—Rigor Versus Simplicity," Journal of Educational and Behavioral Statistics, , vol. 40(1), pages 69-95, February.
    5. Natalie Shlomo & Chris Skinner, 2022. "Measuring risk of re‐identification in microdata: State‐of‐the art and new directions," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 1644-1662, October.
    6. Gerd Ronning, 2014. "Vertraulichkeit und Verfügbarkeit von Mikrodaten," IAW Discussion Papers 101, Institut für Angewandte Wirtschaftsforschung (IAW).
    7. 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.
    8. Humera Razzak & Christian Heumann, 2019. "Hybrid Multiple Imputation In A Large Scale Complex Survey," Statistics in Transition New Series, Polish Statistical Association, vol. 20(4), pages 33-58, December.
    9. 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.
    10. 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.
    11. Florian Meinfelder, 2014. "Multiple Imputation: an attempt to retell the evolutionary process," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 8(4), pages 249-267, November.
    12. Soumya Mukherjee & Aratrika Mustafi & Aleksandra Slavkovi'c & Lars Vilhuber, 2023. "Assessing Utility of Differential Privacy for RCTs," Papers 2309.14581, arXiv.org.
    13. John M. Abowd & Kaj Gittings & Kevin L. McKinney & Bryce E. Stephens & Lars Vilhuber & Simon Woodcock, 2012. "Dynamically Consistent Noise Infusion and Partially Synthetic Data as Confidentiality Protection Measures for Related Time Series," Working Papers 12-13, Center for Economic Studies, U.S. Census Bureau.
    14. Jan Pablo Burgard & Jan-Philipp Kolb & Hariolf Merkle & Ralf Münnich, 2017. "Synthetic data for open and reproducible methodological research in social sciences and official statistics," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 11(3), pages 233-244, December.
    15. Lee, Min Cherng & Mitra, Robin, 2016. "Multiply imputing missing values in data sets with mixed measurement scales using a sequence of generalised linear models," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 24-38.
    16. Mario Trottini, 2008. "Data disclosure limitation as a decision problem," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 109-134.
    17. Ip, Edward H. & Wang, Yuchung J., 2009. "Canonical representation of conditionally specified multivariate discrete distributions," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1282-1290, July.
    18. 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.
    19. 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.
    20. Nowok, Beata & Raab, Gillian M. & Dibben, Chris, 2016. "synthpop: Bespoke Creation of Synthetic Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 74(i11).

    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:iab:iabdpa:200720. 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: IAB, Geschäftsbereich Wissenschaftliche Fachinformation und Bibliothek (email available below). General contact details of provider: https://edirc.repec.org/data/iabbbde.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.