Cyber-physical systems (CPS) are smart computer systems that control or monitor machines through computer-based algorithms, which are vulnerable to both cyber and physical threats. Similar to the growing number of applications, CPS also employ classification algorithms as a tool for data analysis and continuous monitoring of the system. While the utility of data is significantly important in building an accurate and efficient classifier, a free access to original (raw) format of data is a crucial challenge due to privacy constraints. Therefore, it is tremendously important to train classifiers in a private setting in which the privacy of individuals is protected, while data remains still practically useful for building the model. In this chapter, we investigate the application of three privacy preserving models, namely anonymization, Differential Privacy (DP), and cryptography, to privatize data and evaluate the performance of two popular classifiers, Naïve Bayes and Support Vector Machine (SVM) over the protected data. Their performances are compared in terms of accuracy, training construction costs on the same data and in the same private environment. Finally, comprehensive findings on constructing the privacy preserved classifiers are outlined. The attack models against the training data and against the private classifier models are also discussed.