Trends for Mobile IoT Crowdsourcing Privacy and Security in the Big Data Era

From tracking pandemics to applications, such as Google Maps, Uber, environmental monitoring, journalism, healthcare, crisis/disaster response, air quality control, noise and traffic monitoring, urban planning, etc., mobile crowdsourcing (MCS) systems are interweaved with the society and daily lives. This survey outlines major security and privacy challenges in MCS systems along with solutions and approaches. Comprehensive countermeasures, leveraging the capabilities of blockchains, smart contracts, machine learning, games, incentives, spatiotemporal cloaking, etc., are presented to preserve privacy and security of mobile workers, task requestors, and other aspects of crowdsourcing systems. Security recommendations for use cases, such as Industrial IoT, Internet of Vehicles, wireless crowdsensed systems, social crowdsourcing, edge computing, personalized and privacy-preserving recommendation, and mobile worker recruitment are further elaborated.