Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing

TL;DR

This paper explores integrating generative AI into secure mobile crowdsensing to enhance data privacy and security, proposing frameworks and solutions validated through simulations.

Contribution

It introduces a novel framework for using generative AI to protect sensing data content and discusses key research challenges and solutions in this integration.

Findings

Proposed a framework for sensing data content protection using generative AI.

Simulation results demonstrate the effectiveness of the proposed framework.

Identified major research directions for future work in this area.

Abstract

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.