Now You See It, Now You Don’t: Obfuscation of Online Third-Party Information Sharing

Abstract

The practice of sharing online user information with external third parties has become the focal point of privacy concerns for consumer advocacy groups and policy makers. We explore the decisions by websites regarding the obfuscation that they use to make it difficult for users to discover the extent of information sharing. Using a Bayesian model, we shed light on the websites’ incentive to obfuscate user information sharing. We find that as content sensitivity increases, a website reduces its level of obfuscation. Furthermore, more popular websites engage in higher levels of obfuscation than less popular ones. We provide an empirical analysis of obfuscation and user information sharing in News (low content sensitivity) and Health (high content sensitivity) websites and confirm key results from our analytical model. Our analysis illustrates that obfuscation of information sharing is a viable strategy that websites use to improve their profits. History: Ram Ramesh, area editor for Data Science & Machine Learning. Funding: Financial support from the Social Sciences and Humanities Research Council of Canada is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.1266 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0070 ) at http://dx.doi.org/10.5281/zenodo.7336098 .