# Generative AI based Secure Wireless Sensing for ISAC Networks

arXiv: https://arxiv.org/abs/2408.11398 · HTML: https://mlsanity.com/paper/2408.11398/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2024.

## Built on by (most cited first, 20 of 20)

- [RadioDiff-3D: A 3D×3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication](https://mlsanity.com/paper/2507.12166/) (2025)
- [Generative Diffusion Models for Wireless Networks: Fundamental, Architecture, and State-of-the-Art](https://mlsanity.com/paper/2507.16733/) (2025)
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- [Integration of Communication and Computational Imaging](https://mlsanity.com/paper/2410.19415/) (2024)
- [Multi-View Wireless Sensing via Conditional Generative Learning: Framework and Model Design](https://mlsanity.com/paper/2505.12664/) (2025)
- [Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion](https://mlsanity.com/paper/2506.21933/) (2025)
- [Lightweight Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2507.09546/) (2025)
- [Topology Generation of UAV Covert Communication Networks: A Graph Diffusion Approach with Incentive Mechanism](https://mlsanity.com/paper/2508.06746/) (2025)
- [Echo-Conditioned Denoising Diffusion Probabilistic Models for Multi-Target Tracking in RF Sensing](https://mlsanity.com/paper/2510.25464/) (2025)
- [Physical-AI: From Channel Awareness to Environmental Intelligence in 6G Wireless Networks](https://mlsanity.com/paper/2606.20583/) (2026)
- [Unlocking Cross-Scenario Physical Layer Security: A Mixture-of-Experts Framework with Generative Diffusion Models](https://mlsanity.com/paper/2609.26598/) (2026)

## Abstract (arXiv)

Integrated sensing and communications (ISAC) is expected to be a key technology for 6G, and channel state information (CSI) based sensing is a key component of ISAC. However, current research on ISAC focuses mainly on improving sensing performance, overlooking security issues, particularly the unauthorized sensing of users. In this paper, we propose a secure sensing system (DFSS) based on two distinct diffusion models. Specifically, we first propose a discrete conditional diffusion model to generate graphs with nodes and edges, guiding the ISAC system to appropriately activate wireless links and nodes, which ensures the sensing performance while minimizing the operation cost. Using the activated links and nodes, DFSS then employs the continuous conditional diffusion model to generate safeguarding signals, which are next modulated onto the pilot at the transmitter to mask fluctuations caused by user activities. As such, only ISAC devices authorized with the safeguarding signals can extract the true CSI for sensing, while unauthorized devices are unable to achieve the same sensing. Experiment results demonstrate that DFSS can reduce the activity recognition accuracy of the unauthorized devices by approximately 70%, effectively shield the user from the unauthorized surveillance.