  Data driven segmentation is the powerhouse behind the success of online
advertising. Various underlying challenges for successful segmentation have
been studied by the academic community, with one notable exception - consumers
incentives have been typically ignored. This lacuna is troubling as consumers
have much control over the data being collected. Missing or manipulated data
could lead to inferior segmentation. The current work proposes a model of
prior-free segmentation, inspired by models of facility location, and to the
best of our knowledge provides the first segmentation mechanism that addresses
incentive compatibility, efficient market segmentation and privacy in the
absence of a common prior.
