Computational data privacy in wireless networks

Abstract

Wireless networks appeal to privacy eavesdroppers that secretly capture packets from the open medium and read the metadata and data content in search of any type of information through data mining and statistical analysis. Addressing the problems of personal privacy for wireless users will require flexible privacy protection mechanisms adaptively to the frequently varying context. Privacy quantification is prerequisite for enabling a context-aware privacy protection in wireless networks. This paper proposes to quantify the data privacy during wireless communication processes. We give a computational quantification method of data privacy through introducing the concepts of privacy entropy and privacy joint entropy, which permits users and applications to on-demand customize their preferential sensitivity extent of privacy leakage and protection strength. Accordingly, we put forward a data-privacy protection scheme in order to explain how to utilize the proposed computational quantification method of data privacy against the excessive disclosure of data privacy. The results show that the computational quantitation method can effectively characterize the real-time fluctuation of data privacy during communication processes and provide the reliable judgment to context-aware privacy protection, which enables to real-timely control the present data privacy to an anticipated target and to balance a tradeoff between the data privacy and communication efficiency.