The quantification of privacy plays an important role in the privacy protection. Information entropy as a quantitative method of information can be used to solve the problem of privacy measurement. In order to realize the privacy metrics, several models of privacy information entropy are proposed based on Shannon's Information Theory. These models include the basic information entropy model of privacy protection, the information entropy model of privacy protection with adversary, the information entropy model of privacy protection with subjective feelings and multi-source information entropy model of privacy protection. In these models, the information owner is assumed to be the sender, privacy attacker is assumed as to be the recipient, and the privacy disclosure course can be regarded as a communication channel. Based on these assumptions, the entropy, mutual information, conditional entropy, and conditional mutual information are introduced to represent measurement of privacy, privacy disclosure, and privacy and disclosure with background knowledge for the privacy protection system. Furthermore, the quantitative evaluation of privacy protection strength and adversary ability is provided to support quantitative risk assessment for privacy disclosure. Finally, the specific information entropy model, measurement and analysis of privacy protection algorithms, and adversary ability are supplied for location privacy protection application. The proposed models and privacy metrics can be used as fundamental theory for the privacy protection technology and privacy disclosure risk assessment.