Privacy protection strategies in mobile crowdsensing from the framework perspective

In the era of Industrial 5.0, privacy protection in mobile crowdsensing (MCS) becomes even more important to achieve the goals of being human-centric, resilient, and sustainable. To address privacy challenges in MCS environments, this paper first conducts a systematic literature review to identify the research gap in MCS privacy protection and classify privacy protection strategies in terms of key phases of a MCS process. Then a six-dimensional framework for MCS privacy protection integrating user perspective, interaction perspective, and system security perspective is proposed. This comprehensive framework addresses the multifaceted privacy protection limitations identified in MCS process by examining the intersections between these dimensions. Then its effectiveness is demonstrated by case study ‘MCS for personalized healthcare’. This framework balances privacy and data utility, empowers users with transparent policies and control interfaces, and employs AI-driven adaptive security measures. Additionally, it provides a research roadmap for sustainable and resilient privacy protection in the context of Industrial 5.0, offering a comprehensive and user-centric solution to evolving privacy challenges.