Protecting survey data on a consumer level

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.