IDEAS home Printed from https://ideas.repec.org/a/bla/istatr/v84y2016i1p26-42.html
   My bibliography  Save this article

On Invariant Post-randomization for Statistical Disclosure Control

Author

Listed:
  • Tapan K. Nayak
  • Samson A. Adeshiyan

Abstract

No abstract is available for this item.

Suggested Citation

  • Tapan K. Nayak & Samson A. Adeshiyan, 2016. "On Invariant Post-randomization for Statistical Disclosure Control," International Statistical Review, International Statistical Institute, vol. 84(1), pages 26-42, April.
    • Handle: RePEc:bla:istatr:v:84:y:2016:i:1:p:26-42
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/insr.12092
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Ronning, Gerd, 2005. "Randomized response and the binary probit model," Economics Letters, Elsevier, vol. 86(2), pages 221-228, February.
    2. Shlomo, Natalie & Skinner, Chris J., 2010. "Assessing the protection provided by misclassification-based disclosure limitation methods for survey microdata," LSE Research Online Documents on Economics 39119, London School of Economics and Political Science, LSE Library.
    3. van den Hout, Ardo & Kooiman, Peter, 2006. "Estimating the linear regression model with categorical covariates subject to randomized response," Computational Statistics & Data Analysis, Elsevier, vol. 50(11), pages 3311-3323, July.
    4. Duncan, George & Lambert, Diane, 1989. "The Risk of Disclosure for Microdata," Journal of Business & Economic Statistics, American Statistical Association, vol. 7(2), pages 207-217, April.
    5. 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.
    6. Lawrence H. Cox & Alan F. Karr & Satkartar K. Kinney, 2011. "Risk‐Utility Paradigms for Statistical Disclosure Limitation: How to Think, But Not How to Act," International Statistical Review, International Statistical Institute, vol. 79(2), pages 160-183, August.
    7. Di An & Roderick J. A. Little, 2007. "Multiple imputation: an alternative to top coding for statistical disclosure control," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 923-940, October.
    Full references (including those not matched with items on IDEAS)

    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. 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.
    2. 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.
    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. 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.
    5. 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.
    6. Truong-Nhat Le & Shen-Ming Lee & Phuoc-Loc Tran & Chin-Shang Li, 2023. "Randomized Response Techniques: A Systematic Review from the Pioneering Work of Warner (1965) to the Present," Mathematics, MDPI, vol. 11(7), pages 1-26, April.
    7. Goldstein Harvey & Shlomo Natalie, 2020. "A Probabilistic Procedure for Anonymisation, for Assessing the Risk of Re-identification and for the Analysis of Perturbed Data Sets," Journal of Official Statistics, Sciendo, vol. 36(1), pages 89-115, March.
    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.
    9. 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.
    10. Vladimir Hlasny & Paolo Verme, 2018. "Top Incomes and Inequality Measurement: A Comparative Analysis of Correction Methods Using the EU SILC Data," Econometrics, MDPI, vol. 6(2), pages 1-21, June.
    11. Stephen P. Jenkins & Richard V. Burkhauser & Shuaizhang Feng & Jeff Larrimore, 2011. "Measuring inequality using censored data: a multiple‐imputation approach to estimation and inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 174(1), pages 63-81, January.
    12. Bernard Baffour & James Raymer, 2019. "Estimating multiregional survivorship probabilities for sparse data: An application to immigrant populations in Australia, 1981–2011," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 40(18), pages 463-502.
    13. López, Alberto, 2011. "The effect of microaggregation on regression results: an application to Spanish innovation data," MPRA Paper 30403, University Library of Munich, Germany.
    14. Vladimir Hlasny, 2021. "Parametric representation of the top of income distributions: Options, historical evidence, and model selection," Journal of Economic Surveys, Wiley Blackwell, vol. 35(4), pages 1217-1256, September.
    15. Hang J. Kim & Jörg Drechsler & Katherine J. Thompson, 2021. "Synthetic microdata for establishment surveys under informative sampling," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 255-281, January.
    16. Mathias Silva, 2023. "Parametric estimation of income distributions using grouped data: an Approximate Bayesian Computation approach [Working Papers / Documents de travail]," Working Papers hal-04066544, HAL.
    17. Ronning Gerd & Rosemann Martin & Strotmann Harald, 2005. "Post-Randomization Under Test: Estimation of the Probit Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(5), pages 544-566, October.
    18. 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].
    19. Harrison Quick & Scott H. Holan & Christopher K. Wikle, 2018. "Generating partially synthetic geocoded public use data with decreased disclosure risk by using differential smoothing," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 181(3), pages 649-661, June.
    20. Xiao-Bai Li & Sumit Sarkar, 2013. "Class-Restricted Clustering and Microperturbation for Data Privacy," Management Science, INFORMS, vol. 59(4), pages 796-812, April.

    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:bla:istatr:v:84:y:2016:i:1:p:26-42. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/isiiinl.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.