Trading Private Range Counting over Big IoT Data - Cai, Zhipeng; He, Zaobo (Proceedings of the International Conference on Distributed Computing Systems, 2019)
Data privacy arises as one of the most important concerns, facing the pervasive commoditization of big data statistic analysis in Internet of Things (IoT). Current solutions are incapable to thoroughly solve the privacy issues on data pricing and guarantee the utility of statistic outputs. Therefore, this paper studies the problem of trading private statistic results for IoT data, by considering three factors. Specifically, a novel framework for trading range counting results is proposed. The framework applies a sampling-based method to generate approximated counting results, which are further perturbed for privacy concerns and then released. The results are theoretically proved to achieve unbiasedness, bounded variance, and strengthened privacy guarantee under differential privacy. Moreover, a pricing approach is proposed for the traded results, which is proved to be immune against arbitrage attacks. The framework is evaluated by estimating the air pollution levels with different ranges on 2014 CityPulse Smart City datasets. The analysis and evaluation results demonstrate that our framework greatly reduces the error of range counting approximation; and the optimal perturbation approach enables that the private counting satisfies the specified approximation degree while providing strong privacy guarantee.