Segmentation, incentives, and privacy

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.