PiMiCo: Privacy Preservation via Migration in Collaborative Mobile Clouds

The proliferation of mobile devices and mobile clouds coupled with a multitude of their sensing abilities is creating interesting possibilities, the sensing capabilities are creating different types and fidelities of data in a geographically distributed manner that can be used to build new kinds of peer-to-peer applications. However, the data generated by these mobile devices can be personal and of a highly confidential nature. While very interesting possibilities exist for collaborating on the diverse, shared data in real time, privacy policies on the data sharing, transport, as well as usage must be clearly specified and respected. The goal of this work is to introduce a privacy preserving data centric programming model for building collaborative applications in large scale mobile clouds and discuss its design. Our work introduces several concepts and leverages privacy annotations and a transparent execution migration framework to achieve our goals. We also present an evaluation using several applications demonstrating that overheads are minimal and can be used in a real-time setting.

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