IDEAS home Printed from https://ideas.repec.org/a/spr/stpapr/v56y2015i4p1055-1069.html
   My bibliography  Save this article

Respondent privacy and estimation efficiency in randomized response surveys for discrete-valued sensitive variables

Author

Listed:
  • Mausumi Bose

Abstract

In some socio-economic surveys, data are collected on sensitive issues such as tax evasion, criminal conviction, drug use, etc. In such surveys, direct questioning of respondents is not of much use and the randomized response technique is used instead. A few researchers have studied the issue of privacy protection for surveys where the objective is to estimate the proportion of persons bearing the sensitive trait. Not much is known about respondent protection when the variable under study is a discrete quantitative variable and the objective is to estimate the population mean. In this article we study this issue. We propose a scheme for this issue and a measure of privacy. We show that given a stipulated level of this privacy measure, we can determine the parameter of the randomization device so as to maximize the efficiency of estimation, while guaranteeing the desired level of privacy protection. Copyright Springer-Verlag Berlin Heidelberg 2015

Suggested Citation

  • Mausumi Bose, 2015. "Respondent privacy and estimation efficiency in randomized response surveys for discrete-valued sensitive variables," Statistical Papers, Springer, vol. 56(4), pages 1055-1069, November.
    • Handle: RePEc:spr:stpapr:v:56:y:2015:i:4:p:1055-1069
    DOI: 10.1007/s00362-014-0624-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00362-014-0624-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s00362-014-0624-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Raghunath Arnab, 2007. "Randomized response techniques for complex survey designs," Statistical Papers, Springer, vol. 48(2), pages 349-349, April.
    2. Sabrina Giordano & Pier Perri, 2012. "Efficiency comparison of unrelated question models based on same privacy protection degree," Statistical Papers, Springer, vol. 53(4), pages 987-999, November.
    3. Jong-Min Kim & Matthew Elam, 2007. "A stratified unrelated question randomized response model," Statistical Papers, Springer, vol. 48(2), pages 215-233, April.
    4. Lucio Barabesi & Sara Franceschi & Marzia Marcheselli, 2012. "A randomized response procedure for multiple-sensitive questions," Statistical Papers, Springer, vol. 53(3), pages 703-718, August.
    5. Giancarlo Diana & Pier Perri, 2011. "A class of estimators for quantitative sensitive data," Statistical Papers, Springer, vol. 52(3), pages 633-650, August.
    6. Sanghamitra Pal, 2008. "Unbiasedly estimating the total of a stigmatizing variable from a complex survey on permitting options for direct or randomized responses," Statistical Papers, Springer, vol. 49(2), pages 157-164, April.
    7. Raghunath Arnab & Georg Dorffner, 2007. "Randomized response techniques for complex survey designs," Statistical Papers, Springer, vol. 48(1), pages 131-141, January.
    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. Horng-Jinh Chang & Mei-Pei Kuo, 2012. "Estimation of population proportion in randomized response sampling using weighted confidence interval construction," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(5), pages 655-672, July.
    2. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2013. "Design-based distribution function estimation for stigmatized populations," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(7), pages 919-935, October.
    3. Lucio Barabesi & Giancarlo Diana & Pier Perri, 2015. "Gini index estimation in randomized response surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(1), pages 45-62, January.
    4. Kumari Priyanka & Pidugu Trisandhya & Richa Mittal, 2018. "Dealing sensitive characters on successive occasions through a general class of estimators using scrambled response techniques," METRON, Springer;Sapienza Università di Roma, vol. 76(2), pages 203-230, August.
    5. Tanveer A. Tarray & Housila P. Singh & Zaizai Yan, 2017. "A Dexterous Optional Randomized Response Model," Sociological Methods & Research, , vol. 46(3), pages 565-585, August.
    6. Sanghamitra Pal & Arijit Chaudhuri & Dipika Patra, 2020. "How privacy may be protected in optional randomized response surveys," Statistics in Transition New Series, Polish Statistical Association, vol. 21(2), pages 61-87, June.
    7. Shu-Hui Hsieh & Shen-Ming Lee & Su-Hao Tu, 2018. "Randomized response techniques for a multi-level attribute using a single sensitive question," Statistical Papers, Springer, vol. 59(1), pages 291-306, March.
    8. Heiko Groenitz, 2015. "Using prior information in privacy-protecting survey designs for categorical sensitive variables," Statistical Papers, Springer, vol. 56(1), pages 167-189, February.
    9. María del Mar García Rueda & Pier Francesco Perri & Beatriz Rodríguez Cobo, 2018. "Advances in estimation by the item sum technique using auxiliary information in complex surveys," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 455-478, July.
    10. Andreas Quatember, 2012. "An extension of the standardized randomized response technique to a multi-stage setup," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(4), pages 475-484, November.
    11. Kumari Priyanka & Pidugu Trisandhya, 2019. "Modelling Sensitive Issues On Successive Waves," Statistics in Transition New Series, Polish Statistical Association, vol. 20(1), pages 41-65, March.
    12. Priyanka Kumari & Trisandhya Pidugu, 2019. "Modelling Sensitive Issues On Successive Waves," Statistics in Transition New Series, Statistics Poland, vol. 20(1), pages 41-65, March.
    13. Singh, Housila P. & Tarray, Tanveer A., 2014. "A dexterous randomized response model for estimating a rare sensitive attribute using Poisson distribution," Statistics & Probability Letters, Elsevier, vol. 90(C), pages 42-45.

    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:spr:stpapr:v:56:y:2015:i:4:p:1055-1069. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.