PDL Abstract

Challenges in Security and Privacy for Mobile Edge-Clouds

Carnegie Mellon University Parallel Data Lab Technical Report CMU-PDL-13-113. October, 2013.

Jiaqi Tan, Rajeev Gandhi, Priya Narasimhan

Carnegie Mellon University


Mobile devices such as smartphones and tablets are ubiquitous today, and many of them possess significant computation power, powerful sensors such as high-resolution cameras and GPS sensors, and a wealth of sensor data such as photos, videos, and location information. Collections of mobile devices in close geographical proximity present both opportunities and challenges for mobile applications: the enormous collection of mobile devices presents an extremely rich source of user-generated content as well as collective computation power, but these devices are mutually distrusting, and security and privacy concerns, amongst many other obstacles, prevent users from cooperating with other distrusting entities to exploit both the available computation power and data. In this paper, we articulate and describe some of the security and privacy challenges which currently prevent us from leveraging the collective data and computational power available in collections of mobile devices belonging to mutually distrusting users. By addressing these security and privacy challenges, we envision that a new class of applications can be developed which leverage the collective mobile devices available in close geographical proximity: mutually distrusting users would be willing to participate in such public computations with sufficient security and privacy safeguards, enabling novel applications.

KEYWORDS: Mobile Computing, Edge-Clouds, Security, Privacy, Hyrax, MapReduce

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