Privacy-preserving mechanisms for location privacy in mobile crowdsensing: A survey

Abstract

With the advancement in communication techniques and sensor technologies, mobile crowdsensing (MCS)—one of the most successful applications of crowdsourcing—has recently become a powerful tool to solve complex and scalable sensing problems. Generally, MCS is a location-aware crowdsourcing technique in which participating workers must physically move to a specific location to complete tasks. Hence, workers must disclose information regarding their current true location to service providers. However, location information may contain sensitive data; therefore, most workers are not comfortable—or are even reluctant—to provide their exact location information to service providers because of privacy concerns. This is perceived as the most significant challenge faced in MCS. Thus, guaranteeing location privacy is essential for attracting more participants to actively participate in MCS. Accordingly, extensive studies have been conducted in the past few years to protect the location privacy of participating workers in MCS. In this study, we comprehensively survey the state-of-the-art mechanisms for protecting the location privacy of workers in MCS. We divide the location protection mechanisms into three categories depending on the nature of their algorithm and compare them from the viewpoints of architecture, privacy, computational overhead, and utility. Moreover, we discuss certain promising future research directions to spur further research in this area.