Consumer privacy in digital platforms

Abstract

Recently, the emerging platform economy has drastically altered societal norms and attracted scrutiny from relevant regulatory bodies. The contentious issue at hand involves the platforms’ collection and utilization of consumer privacy information. To address this, our research employs the theory of information design to examine the protection of consumer privacy within the platform economy, with a specific focus on User-Generated Content (UGC) platforms.

UGC platforms present content creators with invaluable user data, thereby enabling the production of content that closely corresponds with user preferences. Nevertheless, this benefit is counterbalanced by the inevitable leakage of user privacy, to varying degrees. Consequently, platform users find themselves navigating a complex trade-off between personal privacy and consumption utility. If a significant emphasis is placed on personal privacy, the platforms’ collection and disclosure of such information could instigate user withdrawal. Our research interest lies in this intricate tripartite negotiation over user privacy information between platforms, content creators, and users, in addition to the influence of government privacy regulations.

To dissect these complexities, we incorporate the theory of information design. Herein, content creators are classified as information senders, users as receivers, and the match quality between newly created content and users as an uncertain state. Elements such as content previews are designated as signals. Our approach extends the classic Bayesian persuasion model (Kamenica and Gentzkow, 2011) by integrating user types (e.g., usage habits, preferences) as the receiver’s private information. Platforms are able to collect and commercialize (parts of) the users’ privacy information. Content creators then have the discretion to purchase this privacy information and subsequently design differential signals for various user types. By incorporating a distaste for privacy loss into the user’s utility function and utilizing Kullback-Leibler divergence to gauge the perceived privacy loss among different user types (Eilat et al., 2021), our model explores these dynamics. Through resolution of this model, we aim to provide robust insights into consumer privacy challenges in the platform economy, data strategies of UGC platforms, and the consequential impacts of privacy policies.

Date of Award	2023
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Xu ZHANG (Supervisor) & Jia LIU (Supervisor)