Data Privacy and Cybersecurity in Mobile Crowdsensing

Mobile crowdsensing (MCS) has emerged as a pivotal element in contemporary communication technology, witnessing substantial growth recently. The advent of 5G, the Internet of Things (IoT), and edge computing has propelled MCS researchers to achieve enhanced sensing efficiency and broaden its application spectrum across various domains such as environmental monitoring, traffic management, and healthcare. However, despite these advantages, MCS confronts significant security and privacy challenges due to its open and diverse nature. Critical concerns encompass data leakage, unauthorized access, data tampering, and cross-network attacks. These issues can severely compromise the stability, privacy, and security of MCS systems. Furthermore, the dynamic mobility of users and devices within MCS introduces additional complexity to conventional security measures, particularly concerning communication and cross-domain access control. To tackle these challenges, researchers have devised several strategies aimed at bolstering the security and privacy of MCS systems. These novel protection mechanisms offer distinct benefits over traditional approaches. They are capable of securing data even with constrained computational and communication resources, enhancing system flexibility, and effectively thwarting sophisticated cyberattacks. These strategies provide both theoretical and practical underpinnings for fortifying MCS security and lay a robust foundation for the field's future evolution.