Computational Habitual Privacy And Modeling Privacy-awareness Social Behavior Network

The increasingly networked human society requires that human beings have a clear understanding and control over the structure,nature and behavior of various social networks.There is a tendency towards privacy in the study of network evolutions because privacy disclosure behavior in the network has gradually developed into a serious concern.For this purpose,we extended information theory and proposed a brand-new concept about so-called "habitual privacy" to quantitatively analyze privacy exposure behavior and facilitate privacy computation.We emphasized that habitual privacy is an inherent property of the user and is correlated with their habitual behaviors and their sensitivity to privacy exposure.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.According to the definition,we give the corresponding calculation framework,and applied the presented computing framework 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.The widely approved driving force in recent modeling complex networks is originated from activity.Thus,we propose the privacy-driven model through synthetically considering the activity impact and habitual privacy underlying the decision process.The model mainly considers three factors: the cost of privacy at the expense of establishing new connections,convergence in the process of development,and the impact of the initial state of the network.Privacy-driven model facilitates to more accurately capture highly dynamical network behaviors and figure out the complex evolution process,allowing a profound understanding of the evolution of network driven by privacy.