Design and Evaluation of SensorSafe: A Framework for Achieving Behavioral Privacy in Sharing Personal Sensory Information

Continuous collection of sensory information using smartphones and body-worn sensors is now feasible with recent advancement of technologies. Sharing such personal information enables many useful applications such as medical behavioral studies, personal health-care, and participatory sensing. However, sharing such information along with inferences that can be drawn from the data increases user´s various privacy concerns. This paper proposes SensorSafe, an application framework that enables users to share adequate amounts of their private data and supports obfuscation of sensitive information to protect user privacy. Our framework provides rule-based sharing with context-awareness and conflicting rule detection. In addition, our framework includes several optimization techniques for database processing of rule-based sharing and data obfuscation. We evaluate the optimization techniques with a large amount of accelerometer data from the fine-grained posture recognition application, which is about 6.25 GB.