Estimating the future location of moving objects using different estimation models, such as linear or probabilistic models, has been investigated extensively. However, the location estimations of those models are generally not comparable. For instance, one model might return a position for some object, another one a Gaussian probability distribution, and a third one a uniform distribution. Similar issues arise for query answers. In this paper, we examine the question how estimations of different models can be compared. To do so, we propose a general model based on the central limit theorem. This allows handling different PDF-based approaches as well as models from the other groups (i.e., linear estimations) in a unified manner. Furthermore, we show how to inject privacy into the general model, a fundamental pre-requisite for user acceptance. Thus, we support well-known approaches like k-anonymity and spatial obfuscation. Based on our general model, we conduct a comprehensive experimental study considering a real-world road network; comparing models form different groups for the first time. Our results, for instance, reveal that esti ... mehrmation models based on individual velocity profiles are not necessarily better than models, which estimate the future location of objects only based on their direction. In more abstract terms, our general model allows comparison of estimation models that could not be compared before and gives way to build models that solve the privacy-accuracy challenge.