Using mobile phones to determine transportation modes
ACM Transactions on Sensor Networks (TOSN)
SensLoc: sensing everyday places and paths using less energy
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
The Jigsaw continuous sensing engine for mobile phone applications
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Mobile apps: it's time to move up to CondOS
HotOS'13 Proceedings of the 13th USENIX conference on Hot topics in operating systems
Exploring social context with the wireless rope
OTM'06 Proceedings of the 2006 international conference on On the Move to Meaningful Internet Systems: AWeSOMe, CAMS, COMINF, IS, KSinBIT, MIOS-CIAO, MONET - Volume Part I
MaskIt: privately releasing user context streams for personalized mobile applications
SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
A framework for context-aware privacy of sensor data on mobile systems
Proceedings of the 14th Workshop on Mobile Computing Systems and Applications
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Smart phones with increased computation and sensing capabilities have enabled the growth of a new generation of applications which are organic and designed to react depending on the user contexts. These contexts typically define the personal, social, work and urban spaces of an individual and are derived from the underlying sensor measurements. The shared context streams therefore embed in them information, which when stitched together can reveal behavioral patterns and possible sensitive inferences, raising serious privacy concerns. In this paper, we propose a model based technique to capture the relationship between these contexts, and better understand the privacy implications of sharing them. We further demonstrate that by using a generative model of the context streams we can simultaneously meet the utility objectives of the context-aware applications while maintaining individual privacy. We present our current implementation which uses offline model learning with online inferencing performed on the smart phone. Preliminary results are presented to provide proof-of-concept of our proposed technique.