Generative AI for Secure and Privacy-Preserving Mobile Crowdsensing

TL;DR

This work investigates the integration of generative AI with secure and privacy-preserving mobile crowdsensing (SPPMCS) to bolster data security and privacy during sensing tasks. It surveys preliminaries for SPPMCS and generative AI, then presents a framework for deploying generative AI-enabled SPPMCS, including a GDM-based sensing data content protection approach and a case study on vehicle image data that introduces a Privacy-Preserving Utility Index (PPUI) to balance privacy with task accuracy. The paper identifies key research focuses—defense against malicious data injection, illegal authorization, and malicious spectrum manipulation, as well as privacy protection for data content and MST identity/location—and discusses challenges such as computation, latency, and regulatory concerns. Overall, it provides a roadmap for leveraging generative AI to improve security defenses and privacy preservation in distributed sensing systems, along with an experimental demonstration and open directions for future work.

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.

Figures (3)

• Figure 1: The role of generative AI in security defense and privacy preservation aspects for SPPMCS applications. In the security defense aspect, generative AI can help handle malicious data injection, illegal authorization, and physical layer malicious spectrum manipulation on the task distribution, task receiving, data collection, and data uploading stages. As for the privacy preservation issue, on the data collection and data uploading stages, the generative AI-enabled SPPMCS can alleviate the privacy preservation concerns on sensing data, and MST's identification and location information.
• Figure 2: A framework for sensing data content protection in generative AI-enabled SPPMCS, where the GDM model is adopted to generate synthetic vehicle images. In the GDM-based synthetic image generation process, the feature of an original image can be learned by adding noise in the forward diffusion stage. On this basis, denoising operation can generate random noise to the target image in the reverse diffusion stage. Since synthetic data contains no privacy information of the real-world, and by substituting real-world images with synthetic images, the sensing data privacy issues can be alleviated.
• Figure 3: Performance evaluation of the proposed framework. (a) Training process of GDM and baseline models. Left: GDM. Right: GAN. (b) Images generated by GDM and baseline models. Left: GDM. Right: GAN. (c) Precision recall performance for YOLOv3 detection model with different percentage of synthetic data for training dataset. (d) Privacy-preserving utility index performance with different percentage of synthetic data for training dataset.