Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Journal Article · Tue Jan 01 00:00:00 EST 2013 · Pervasive and Mobile Computing

OSTI ID:1089270

Forrest, Stephanie ^[1]; He, Wenbo ^[2]; Groat, Michael ^[1]; Edwards, Benjamin ^[1]; Horey, James L ^[3]

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1. University of New Mexico, Albuquerque
2. University of Nebraska, Lincoln
3. ORNL

Participatory sensing applications rely on individuals to share personal data to produce aggregated models and knowledge. In this setting, privacy concerns can discourage widespread adoption of new applications. We present a privacy-preserving participatory sensing scheme based on negative surveys for both continuous and multivariate categorical data. Without relying on encryption, our algorithms enhance the privacy of sensed data in an energy and computation efficient manner. Simulations and implementation on Android smart phones illustrate how multidimensional data can be aggregated in a useful and privacy-enhancing manner.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1089270
• Journal Information: Pervasive and Mobile Computing, Vol. 9, Issue 3
• Country of Publication: United States
• Language: English