Privacy Harms

Abstract

Privacy loss is central to privacy law scholarship, but a clear definition of the concept remains elusive. We present a model that both captures the essence of privacy loss and can be easily applied to policy evaluations and doctrinal debates. To do so, we use standard Bayesian statistics to formalize a key intuition: that information privacy is fundamentally linked to how much other people know about you. A key advantage of our model is that, for the first time, it takes privacy preferences seriously while maintaining tractability. Another key advantage is that, by viewing privacy as a continuum, it is more realistic and is better suited for evaluating “gray areas” than prior models.

We apply this framework to two central areas of privacy law: the common law privacy tort and the Fourth Amendment’s third party doctrine. In the tort context, we first show how our proposal helps to clarify current law, and then use it to distinguish between the two interests protected by the privacy tort: privacy interests and reputational interests. We then propose a simple framework for judges to use in providing remedies for both classes of claims. We then move on to the third party doctrine. We show that many of the shortcomings associated with the doctrine stem from the misguided assumption that privacy is dichotomous rather than a spectrum, as in our model. We then liken this to the standard of care familiar from tort law, and show how the current doctrine results in the equivalent of a strict liability standard, rather than a more appropriate negligence-based standard.

Keywords: Information privacy, Law & Economics, Tort Law, Fourth Amendment Law, Bayesian updating