Skip to main content
SpringerLink
Log in

Protecting survey data on a consumer level

  • Original Article
  • Published:
Journal of Marketing Analytics Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Notes

  1. Note that we are not singling Austin out for criticism per se; many similar databases are publicly accessible.

  2. 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.

References

  • Abowd, J.M., M.J. Schneider, and L. Vilhuber. 2013. Differential privacy applications to Bayesian and linear mixed model estimation. Journal of Privacy and Confidentiality 5 (1).

  • Bambauer, J., K. Muralidhar, and R. Sarathy. 2013. Fool’s gold: An illustrated critique of differential privacy. Vanderbilt Journal of Entertainment & Technology Law 16: 701.

    Google Scholar 

  • Blair, Graeme, Kosuke Imai, and Yang-Yang Zhou. 2015. Design and analysis of the randomized response technique. Journal of the American Statistical Association 110 (511): 1304–1319.

    Article  Google Scholar 

  • Churchill Jr., Gilbert A. 1979. A paradigm for developing better measures of marketing constructs. Journal of Marketing Research 16 (1): 64–73.

    Article  Google Scholar 

  • City of Austin. 2016. AAQoL: AustinTexas.gov—The official website of the city of Austin. http://www.austintexas.gov/asianlifeaustin. Accessed 21 Oct 2017.

  • City of Austin. 2017a. AAQOL survey monkey answers Nov 7 update: Open data: City of Austin Texas. https://data.austintexas.gov/City-Government/AAQOL-Survey-Monkey-Answers-Nov7-Update/i3d7-gc2g.

  • City of Austin. 2017b. Open data: AustinTexas.gov—The official website of the city of Austin. http://austintexas.gov/opendata. Accessed 21 Oct 2017

  • De Jong, Martijn G., Rik Pieters, and Jean-Paul Fox. 2010. Reducing social desirability bias through item randomized response: An application to measure underreported desires. Journal of Marketing Research 47 (1): 14–27.

    Article  Google Scholar 

  • Dhar, Tirtha, and Kathy Baylis. 2011. Fast-food consumption and the ban on advertising targeting children: The Quebec experience. Journal of Marketing Research 48 (5): 799–813.

    Article  Google Scholar 

  • Dickson, John P., and Douglas L. Maclachlan. 1996. Fax surveys: Return patterns and comparison with mail surveys. Journal of Marketing Research 33 (1): 108–113.

    Article  Google Scholar 

  • Donnelly, Laura. 2014. Hospital records of All NHS patients sold to insurers. Telegraph. http://www.telegraph.co.uk/news/health/news/10656893/Hospital-records-of-all-NHS-patients-sold-to-insurers.html. Accessed 23 Feb

  • Duchi, John C., Michael I. Jordan, and Martin J. Wainwright. 2018. Minimax optimal procedures for locally private estimation. Journal of the American Statistical Association 113 (521): 182–201.

    Article  Google Scholar 

  • Dwork, Cynthia, et al. 2006. Calibrating noise to sensitivity in private data analysis. Theory of cryptography conference. Springer, Berlin.

    Chapter  Google Scholar 

  • Furse, David H., and David W. Stewart. 1982. Monetary incentives versus promised contribution to charity: New evidence on mail survey response. Journal of Marketing Research 19 (3): 375–380.

    Article  Google Scholar 

  • Holtrop, Niels, Jaap E. Wieringa, Maarten J. Gijsenberg, and Peter C. Verhoef. 2017. No future without the past? Predicting churn in the face of customer privacy. International Journal of Research in Marketing 34 (1): 154–172.

    Article  Google Scholar 

  • Iacobucci, Dawn, and G.A. Churchill Jr. 2015. Marketing research: Methodological foundations, 11th ed. Nashville, TN: Earlie Lite Books.

    Google Scholar 

  • Iman, Ronald L., and W.J. Conover. 1982. A distribution-free approach to inducing rank correlation among input variables. Communications in Statistics-Simulation and Computation 11 (3): 311–334.

    Article  Google Scholar 

  • Jang, Yuri. 2016. Asian Americans in Austin: Final report of the Asian American Quality of Life (AAQoL) Survey. http://www.austintexas.gov/sites/default/files/files/Communications/4.2_FINAL_AA_in_Austin_report_from_UT.pdf. Accessed 21 Oct 2017.

  • Kannan, P.K. 2017. Digital marketing: A framework, review and research agenda. International Journal of Research in Marketing 34 (1): 22–45.

    Article  Google Scholar 

  • Li, Bai, Vishesh Karwa, Aleksandra Slavković, and Rebecca Steorts. 2018. A privacy preserving algorithm to release sparse high-dimensional histograms. Journal of Privacy and Confidentiality 8 (1).

  • Little, Roderick J.A. 1993. Statistical analysis of masked data. Journal of Official Statistics 9 (2): 407–426.

    Google Scholar 

  • Machanavajjhala, A., J. Gehrke, D. Kifer, and M. Venkitasubramaniam. 2006. l-Diversity: Privacy beyond k-anonymity. In 22nd international conference on data engineering (ICDE’06), 24. IEEE.

  • Machanavajjhala, A., D. Kifer, J. Abowd, J. Gehrke, and L. Vilhuber. 2008. Privacy: Theory meets practice on the map. In Proceedings of the 2008 IEEE 24th international conference on data engineering, 277–286. IEEE Computer Society.

  • Martin, K.D., A. Borah, and R.W. Palmatier. 2017. Data privacy: Effects on customer and firm performance. Journal of Marketing 81 (1): 36–58.

    Article  Google Scholar 

  • Mildenhall, Stephen J. 2006. Correlation and aggregate loss distributions with an emphasis on the Iman-Conover method. Casualty Actuarial Society Forum 103–204.

  • Miller, Amalia R., and Catherine Tucker. 2009. Privacy protection and technology diffusion: The case of electronic medical records. Management Science 55 (7): 1077–1093.

    Article  Google Scholar 

  • Muralidhar, K., and R. Sarathy. 2006. Data shuffling—A new masking approach for numerical data. Management Science 52 (5): 658–670.

    Article  Google Scholar 

  • Ng, Irene C.L., and Susan Y.L. Wakenshaw. 2017. The internet-of-things: Review and research directions. International Journal of Research in Marketing 34 (1): 3–21.

    Article  Google Scholar 

  • Posada, David. 2002. Evaluation of methods for detecting recombination from DNA sequences: Empirical data. Molecular Biology and Evolution 19 (5): 708–717.

    Article  Google Scholar 

  • Rindfleisch, Aric, Alan J. Malter, Shankar Ganesan, and Christine Moorman. 2008. Cross-sectional versus longitudinal survey research: Concepts, findings, and guidelines. Journal of Marketing Research 45 (3): 261–279.

    Article  Google Scholar 

  • Schneider, Matthew J., Sharan Jagpal, Sachin Gupta, Shaobo Li, and Yu. Yan. 2017. Protecting customer privacy when marketing with second-party data. International Journal of Research in Marketing 34 (3): 593–603.

    Article  Google Scholar 

  • Schneider, Matthew, Sharan Jagpal, Sachin Gupta, Yan Yu, and Shaobo Li. 2018. A flexible method for protecting marketing data: An application to point-of-sale data. Marketing Science, Articles in Advance.

  • Sweeney, Latanya. 2000. Uniqueness of simple demographics in the U.S. population. Technical Report, Carnegie Mellon University.

  • Sweeney, Latanya. 2002. k-anonymity: A model for protecting privacy. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 10 (5): 557–570.

    Article  Google Scholar 

  • Vitale, R. A. 1990. On stochastic dependence and a class of degenerate distributions. Lecture Notes-Monograph Series, 459–469.

  • Warner, Stanley L. 1965. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association 60 (309): 63–69.

    Article  Google Scholar 

  • Wang, Yue, Kifer, Daniel, Lee, Jaewoo. 2019. Differentially private confidence intervals for empirical risk minimization. Journal of Privacy and Confidentiality 9(1)

  • Wedel, Michel, and P.K. Kannan. 2016. Marketing analytics for data-rich environments. Journal of Marketing 80 (6): 97–121.

    Article  Google Scholar 

  • Weijters, Bert, and Hans Baumgartner. 2012. Misresponse to reversed and negated items in a Survey: A review. Journal of Marketing Research 49 (5): 737–747.

    Article  Google Scholar 

  • Wynn-Evans, Charles. 2014. Risking a criminal record for misusing confidential information: People management magazine online. http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2014/10/13/risking-a-criminal-record-for-misusing-confidential-information.aspx. Accessed 13 Oct

Download references

Author information

Authors and Affiliations

  1. Department of Decision Sciences, LeBow College of Business, Drexel University, 3220 Market Street, Philadelphia, PA, 19104, USA

    Matthew J. Schneider

  2. Owen Graduate School of Management, Vanderbilt University, 401 21st Avenue South, Nashville, TN, 37203, USA

    Dawn Iacobucci

Authors
  1. Matthew J. Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dawn Iacobucci
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dawn Iacobucci.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Schneider, M.J., Iacobucci, D. Protecting survey data on a consumer level. J Market Anal 8, 3–17 (2020). https://doi.org/10.1057/s41270-020-00068-6

Download citation

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1057/s41270-020-00068-6

Keywords

Search

Navigation