A Quantifying Metric for Privacy Protection Based on Information Theory

In open and dynamic computing environments, the entities that make connections and interactions may know little about each other or without prior knowledge. So, certain level of trust must be established. This may require that an entity request some information from other entities that probably involves privacy. Therefore, quantifying privacy loss and trust gain is a meaningful and important subject. Although some work has already been done in this area, it has failed to consider the relationship between privacy information and dynamic trust variation during quantifying process. In this paper, we propose a novel quantifying metric for privacy protection based-on information theory in which we investigate dynamic trust variation to lower privacy loss while achieving more trust gain when exchange information. Simulation results show that our metric can achieve the goal well.