  High-resolution individual geolocation data passively collected from mobile
phones is increasingly sold in private markets and shared with researchers.
This data poses significant security, privacy, and ethical risks: it's been
shown that users can be re-identified in such datasets, and its collection
rarely involves their full consent or knowledge. This data is valuable to
private firms (e.g. targeted marketing) but also presents clear value as a
public good. Recent public interest research has demonstrated that
high-resolution location data can more accurately measure segregation in cities
and provide inexpensive transit modeling. But as data is aggregated to mitigate
its re-identifiability risk, its value as a good diminishes. How do we rectify
the clear security and safety risks of this data, its high market value, and
its potential as a resource for public good? We extend the recently proposed
concept of a tradeoff curve that illustrates the relationship between dataset
utility and privacy. We then hypothesize how this tradeoff differs between
private market use and its potential use for public good. We further provide
real-world examples of how high resolution location data, aggregated to varying
degrees of privacy protection, can be used in the public sphere and how it is
currently used by private firms.
