On the properties that characterize privacy

Privacy, in the sense of control over access to one’s personal information, is a central concern in the context of online decision making, both in general and in relation to online platforms in particular. For at least some agents, a belief that one online platform jeopardizes users’ privacy more than another may tip the scales in favor of the latter. Thus, understanding how privacy considerations come into play is central for any economic or social analysis. To this end, we study how agents rank online platforms (or mechanisms, as we call them) from a privacy perspective. We propose a very simple model of privacy-jeopardizing mechanisms, along with a normative methodology for understanding how these mechanisms are ranked. Similarly to classic work in decision theory, we postulate several axioms that we believe a privacy order should satisfy, and then characterize the set of orders that comply with these axioms. These orders turn out to be related to the notion of f-divergence from information theory, one example of which is KL divergence. We test the usefulness of our theoretical result by using it to rank clustering models based on data provided by the Recommendation Team at Microsoft Research.