Private Data Trading Towards Range Counting Queries in Internet of Things

The data collected in Internet of Thing (IoT) systems (IoT data) have stimulated dramatic extension to the boundary of commercialized data statistic analysis. However, huge volumes of devices bring large scales of data, constituting heavy burdens for data exchange. Even worse, contents in IoT systems are also sensitive as they are usually linked to private physical status of data contributors. Therefore, this paper proposes a novel framework for the range counting trading over IoT networks by jointly considering data utility, bandwidth consumption, and privacy preservation. This paper first proposes a novel sampling-based method with histogram sketching for range counting estimation. Then the framework adopts a perturbation mechanism that can further preserve the results under differential privacy. Finally, two types of pricing strategies for range counting trading are introduced for different circumstances, providing holistic consideration on how the parameters given in the estimator should be used for data trading. The framework is evaluated by estimating the air pollution levels and the traffic levels with different ranges on the 2014 CityPulse Smart City datasets.