Preserving Data Query Privacy in Mobile Mashups through Mobile Cloud Computing

Abstract

Mobile mashups promise great data aggregation and processing capabilities for all end users. During the data collection procedures, some data providers fail to protect confidentiality and privacy of user queries and transmit information in plain text. This enables attackers to eavesdrop on networks and compromise user information. Since mobile mashups can adopt server-side, client-side, or hybrid architectures, no one-size-fits-all solutions can be designed to solve this problem. In this paper, we propose to design two mechanisms using mobile clouds to preserve data query privacy in mobile mashups. For server-side mashups, we propose to use dynamically created virtual machines as proxies to process data collection and aggregation in order to prevent information leakage through eavesdropping. For client-side mashups, we propose to use live migration of the application level virtual machines into mobile cloud to hide the data collection and aggregation procedures from attackers. We will evaluate the proposed approaches through both analysis and experiments on real platforms.