Feasibility of Structured, Machine-Readable Privacy Notices

This paper offers a novel approach to the long standing problem of the interface of humans and online privacy notices. As literature and practice, and even art, for more than a decade have identified, privacy notices are nearly always ignored and "accepted"with little thought, mostly because it is not practical nor user-friendly to depend on reading a long text simply to access, e.g., a news website. Nevertheless, privacy notices are a central element, often mandated by law.We approach the problem by (partially) relieving the human from the task of inspecting such documents. Because they are documents written in natural language, often legal language, we assess the feasibility of representing privacy notices in a machine-readable format. Should this be feasible, automated processing of notices that still respect individual choices could be enabled. To this end, we manually inspected privacy notices under EU/UK's GDPR from common websites, and designed a JSON schema that captures their structure.