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.
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Note that we are not singling Austin out for criticism per se; many similar databases are publicly accessible.
We have blanked out the actual ZIP code because a colleague mentioned being uncomfortable when reading the precision of identification of this 88-year-old woman. It is precisely this discomfort that data privacy efforts like ours are intended to minimize. However, to respect the fact that a reader could indeed go on to identify this woman, we have blinded that data in the illustration.
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Schneider, M.J., Iacobucci, D. Protecting survey data on a consumer level. J Market Anal (2020). https://doi.org/10.1057/s41270-020-00068-6
- Data protection
- Data privacy
- Survey data
- Personal identification