  Recently, generative AI has attracted much attention from both academic and
industrial fields, which has shown its potential, especially in the data
generation and synthesis aspects. Simultaneously, secure and privacy-preserving
mobile crowdsensing (SPPMCS) has been widely applied in data collection/
acquirement due to an advantage on low deployment cost, flexible
implementation, and high adaptability. Since generative AI can generate new
synthetic data to replace the original data to be analyzed and processed, it
can lower data attacks and privacy leakage risks for the original data.
Therefore, integrating generative AI into SPPMCS is feasible and significant.
Moreover, this paper investigates an integration of generative AI in SPPMCS,
where we present potential research focuses, solutions, and case studies.
Specifically, we firstly review the preliminaries for generative AI and SPPMCS,
where their integration potential is presented. Then, we discuss research
issues and solutions for generative AI-enabled SPPMCS, including security
defense of malicious data injection, illegal authorization, malicious spectrum
manipulation at the physical layer, and privacy protection on sensing data
content, sensing terminals' identification and location. Next, we propose a
framework for sensing data content protection with generative AI, and
simulations results have clearly demonstrated the effectiveness of the proposed
framework. Finally, we present major research directions for generative
AI-enabled SPPMCS.
