  A geo-marketplace allows users to be paid for their location data. Users
concerned about privacy may want to charge more for data that pinpoints their
location accurately, but may charge less for data that is more vague. A buyer
would prefer to minimize data costs, but may have to spend more to get the
necessary level of accuracy. We call this interplay between privacy, utility,
and price \emph{spatial privacy pricing}. We formalize the issues
mathematically with an example problem of a buyer deciding whether or not to
open a restaurant by purchasing location data to determine if the potential
number of customers is sufficient to open. The problem is expressed as a
sequential decision making problem, where the buyer first makes a series of
decisions about which data to buy and concludes with a decision about opening
the restaurant or not. We present two algorithms to solve this problem,
including experiments that show they perform better than baselines.
