ABSTRACT
Information theory models are based on probability theory and statistics features. Information theory often concerns itself with measures of information of the distributions associated with random variables. Important quantities of information are entropy, a measure of information in a single random variable, and mutual information, a measure of information in common between two random variables. This chapter discusses the use of robustness of information theory models with a personalized differential privacy framework. The proposed study shows that our fusion model edges past in performance against the most popular methods to preserve privacy of patients in the cyber-physical ecosystem. The performance metrics used to judge the privacy preservation models are quite reasonable as they offer a significant balance between the various trades-offs occurring during information release from the privacy models.
