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Protecting survey data on a consumer level

Author

Listed:
  • Matthew J. Schneider

    (Drexel University)

  • Dawn Iacobucci

    (Vanderbilt University)

Abstract

This paper offers an easy-to-implement approach to protect multivariate survey data common in marketing, such as attitudes and demographics. Our approach preserves multivariate distributions by releasing a protected data set with privacy protections. The data represent a highly detailed multivariate survey with severe privacy issues that enables us to demonstrate the tradeoff between data utility and data privacy. We create a data privacy metric that quantifies the ability of a data intruder successfully identify survey respondents and their sensitive responses. We provide data privacy measurements for a variety of competitor methods such as sampling and random noise addition and we show that by comparison, our approach can prevent a data intruder from targeting individuals while maintaining a very high level of data utility.

Suggested Citation

  • Matthew J. Schneider & Dawn Iacobucci, 2020. "Protecting survey data on a consumer level," Journal of Marketing Analytics, Palgrave Macmillan, vol. 8(1), pages 3-17, March.
    • Handle: RePEc:pal:jmarka:v:8:y:2020:i:1:d:10.1057_s41270-020-00068-6
    DOI: 10.1057/s41270-020-00068-6
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    References listed on IDEAS

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    Cited by:

    1. Maria Petrescu & Anjala S. Krishen, 2021. "A tribute to our heroes and thoughts about collaborative relationships," Journal of Marketing Analytics, Palgrave Macmillan, vol. 9(2), pages 81-82, June.

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