Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing

Abstract

Recently, generative AI has attracted much attention from both academic and industrial fields, due to its potential especially in data generation and synthesis aspects. Simultaneously, secure and privacy-preserving mobile crowdsensing (SPPMCS) has been widely applied in data collection/acquisition due to advantages of 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 article 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 against malicious data injection, illegal authorization, malicious spectrum manipulation at the physical layer, as well as privacy protection for data content and terminals' identification and location. Next, we propose a framework for sensing data content protection with generative AI. Simulation results have clearly demonstrated the effectiveness of this framework. Finally, we present major research directions for generative AI-enabled SPPMCS.