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Leveraging Graphical Models to Improve Accuracy and Reduce Privacy Risks of Mobile Sensing by Abhinav Parate; Meng-Chieh Chiu; Deepak Ganesan; Benjamin M. Marlin is a scholarly article available to read on EtoBox.

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The proliferation of sensors on mobile phones and wearables has led to a plethora of context classifiers designed to sense the individual's context. We argue that a key missing piece in mobile inference is a layer that fuses the outputs of several classifiers to learn deeper insights into an individual's habitual patterns and associated correlations between contexts, thereby enabling new systems optimizations and opportunities. In this paper, we design CQue, a dynamic bayesian network that operates over classifiers for individual contexts, observes relations across these outputs across time, and identifies opportunities for improving energy-efficiency and accuracy by taking advantage of relations. In addition, such a layer provides insights into privacy leakage that might occur when seemingly innocuous user context revealed to different applications on a phone may be combined to reveal more information than originally intended. In terms of system architecture, our key contribution is a clean separation between the detection layer and the fusion layer, enabling classifiers to solely focus on detecting the context, and leverage temporal smoothing and fusion mechanisms to further boos

Author

Abhinav Parate; Meng-Chieh Chiu; Deepak Ganesan; Benjamin M. Marlin

Publisher

ACM

Published

2013

Language

EN