Research Of Mobile Internet Privacy Disclosure

In the information age, more and more users communicate with the outside through mobile devices. From a single voice calls to SMS, MMS, and data services, the rapid development of mobile communication technology make life more convenient and colorful. At the same time, the security issues of user privacy gradually highlights and users concerned about the privacy of their own also improved.Based on such background, this article makes an additional study on four aspects. These are security of the mobile Internet access environment, transmission network security, device security and service security. After that research, this article summarized the privacy information that may be leaked, and divided them into directly leaked privacy information and indirect leaked privacy, analyze and infer their potential harm. In the subsequent, this article analysis and validate with the indirect privacy information.And then, this article designed two types of analysis method, targeted on position privacy information, behavior privacy information and social network privacy information of indirect leaked type. One is position analysis with right to value analysis method, which make classification of users working days, rest days, using the user’s location in the residence time in the position to calculate the important position of the user at different period. The other is based on ant colony clustering algorithm, used to calculate the user’s behavior and the user’s social network. User behavior and social networks can be abstracted into a model of "individual" and "relationship", so the data can use clustering way to distinguish the user group. Through the definition of different relationships to make user cluster analysis, or algorithm calculates the distance between the user individual. Finally, this article use two typical data sets make verification of the above two types of design algorithm. Use Data set to make the position calculation, the behavior calculated, user circles calculation, closeness and intimacy calculation, and count accuracy of calculations, to prove malicious attacker can use users’data to analysis and excavation, to determine the user’s location point, user behavior, social networking, user social closeness degree and intimacy to a certain extent.