DREAM: Online Control Mechanisms for Data Aggregation Error Minimization in Privacy-Preserving Crowdsensing

Abstract

Nowadays, by integrating the smart devices carried by users with existing communication infrastructures to provide large-scale, fine-grained and complex sensing services, crowdsensing as a novel sensing paradigm has significantly enriched the applications of smart city and promoted the development of Internet of Things (IoT). However, privacy has become skyrocketing concern for crowdsensing and gravely affected the deployment of crowdsensing. In this paper, we present a framework to make the tradeoff between minimizing data aggregation error and guaranteeing system stability by jointly considering the privacy of participants, the randomness of sensing task arrival and the cost of platform. We propose an online control mechanism by exploiting Lyapunov stochastic optimization technique. Additionally, considering that, in reality, it always take different time for different tasks to make sensing decisions, we extend standard Lyapunov stochastic optimization technique to make separate decisions for different types of sensing tasks in consecutive time. Through rigorous theoretical analysis, we prove that our time-average data aggregation error is approximately optimal while still maintaining system stability. By carrying out extensive simulations, we demonstrate the superiority of our proposed mechanisms.