Protecting survey data on a consumer level

Matthew J. Schneider () and Dawn Iacobucci ()
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Matthew J. Schneider: Drexel University
Dawn Iacobucci: Vanderbilt University

Journal of Marketing Analytics, 2020, vol. 8, issue 1, No 2, 3-17

Abstract: 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.

Keywords: Data protection; Data privacy; Survey data; Personal identification (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (1)

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DOI: 10.1057/s41270-020-00068-6

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