How Good Are Privacy Guarantees? Data Sharing, Privacy Preservation, and Platform Behavior

Abstract

Many platforms deploy data collected from users for a multitude of purposes. While some are beneficial to users, others are costly to their privacy. The presence of these privacy costs means that platforms may need to provide guarantees about how and to what extent user data will be harvested for activities such as targeted ads, individualized pricing, and sales to third parties. In this paper, we build a multi-stage model in which users decide whether to share their data based on privacy guarantees. We prove that a simple shuffling mechanism, whereby individual data is fully anonymized with some probability, is optimal from the viewpoint of users. We show that under any shuffling mechanism, there exists a unique equilibrium in which privacy guarantees balance privacy costs and utility gains from the pooling of user data for purposes such as assessment of health risks or product development. Paradoxically, we show that as users' value of pooled data increases, the equilibrium of the game leads to lower user welfare. This is because platforms take advantage of this change to reduce privacy guarantees so much that user utility declines (whereas it would have increased with a given mechanism). Even more strikingly, we show that platforms have incentives to choose data architectures that systematically differ from those that are optimal from user point of view. In particular, we identify class of pivot mechanisms, linking individual privacy to choices by others, which platforms prefer to implement and which make users significantly worse off.

Keywords: Data Markets, Privacy, Shuffling Mechanism, Platform Behavior