Enhancing Privacy in Participatory Sensing Applications with Multidimensional Data

Participatory sensing applications rely on individuals to share local and personal data with others to produce aggregated models and knowledge. In this setting, privacy is an important consideration, lack of which could discourage widespread adoption of many applications. We present a privacy-preserving participatory sensing scheme for continuous and multivariate categorical data which uses neg-ative surveys. Our algorithms conserving energy by avoiding computationally expensive encryption and key management techniques. An implementation on Android smart phones illustrate how the approach can protect the privacy of a participant’s multidimensional data while allowing useful aggregate information to be collected