Computational habitual privacy

Privacy has gradually developed into a serious concern. Although some privacy protection mechanisms are available, it is still a prerequisite to develop an objective and universal evaluation criterion. In fact, we have to know how much the privacy quantity has been already exposed through a human-understandable way. For this purpose, we proposed a series of brand-new concepts about so-called “habitual privacy” to quantitatively analyze privacy exposure behavior. It should be emphasized that habitual privacy is an inherent property of the user and is correlated with their habitual behaviors. Joint privacy quantity is a measurement of the exposed privacy of two habitual behaviors occurring simultaneously on the same occasion. Moreover, cumulative privacy is the accumulative quantity of privacy exposure within a considerable temporal and/or spatial interval. The presented computing framework has been applied to four different empirical data sets. These data sets consisted of massive sample sets obtained from moving trajectories, Bluetooth connections, velocity preferences, and call data records. The results disclosed the characteristics hidden in different conditions of the presented quantification framework and showed the effects of combinations of various related parameters. The proposed computational habitual privacy quantity is expected to establish a theoretical cornerstone for the design of more effective and efficient privacy protection mechanisms.