Health insurance companies prefer to enter new markets in which individuals likely to enroll in their plans have a low annual cost. When deciding which new markets to enter, health cost data for the new markets is unavailable to them, but health cost data for their own enrolled members is available. To address the problem of assessing risk in new markets, i.e., estimating the cost of likely enrollees, we pose a regression problem with demographic data as predictors combined with a novel three-population covariate shift. Since this application deals with health data that is protected by privacy laws, we cannot use the raw data of the insurance company's members directly for training the regression and covariate shift. Therefore, to construct a full solution, we also develop a novel method to achieve fc-anonymity with the workload-driven quality of data distribution preservation achieved through dithered quantization and Rosenblatt's transformation. We illustrate the efficacy of the solution using real-world, publicly available data.