In recent years, there has been rapid growth in mobile devices such as smartphones, and a number of applications are developed specifically for the smartphone market. In particular, there are many applications that are "free" to the user, but depend on advertisement services for their revenue. Such applications include an advertisement module - a library provided by the advertisement service - that can collect a user's sensitive information and transmit it across the network. Such information is used for targeted advertisements, and user behavior statistics. Users accept this business model, but in most cases the applications do not require the user's acknowledgment in order to transmit sensitive information. Therefore, such applications' behavior becomes an invasion of privacy. In our analysis of 1,188 Android applications' network traffic and permissions, 93% of the applications we analyzed connected to multiple destinations when using the network. 61% required a permission combination that included both access to sensitive information and use of networking services. These applications have the potential to leak the user's sensitive information. Of the 107,859 HTTP packets from these applications, 23,309 (22%) contained sensitive information, such as device identification number and carrier name. In an effort to enable users to control the transmission of their private information, we propose a system which, using a novel clustering method based on the HTTP packet destination and content distances, generates signatures from the clustering result and uses them to detect sensitive information leakage from Android applications. Our system does not require an Android framework modification or any special privileges. Thus users can easily introduce our system to their devices, and manage suspicious applications' network behavior in a fine grained manner. Our system accurately detected 94% of the sensitive information leakage from the applications evaluated and produced only 5% false negative results, and less than 3% false positive results.