Geometry-Aligned Differential Privacy for Location-Safe Federated Radio Map Construction
Jijia Tian, Wangqian Chen, Junting Chen, Pooi-Yuen Kam

TL;DR
This paper introduces a geometry-aligned differential privacy method for federated radio map construction, significantly increasing user location privacy while maintaining high model accuracy.
Contribution
It proposes a novel privacy mechanism that aligns noise with the spatial structure of gradients, improving privacy without sacrificing model performance.
Findings
Increases attacker localization error from 30 m to over 180 m.
Only 0.2 dB increase in radio map error compared to baseline.
Provides theoretical convergence guarantees linking privacy and accuracy.
Abstract
Radio maps that describe spatial variations in wireless signal strength are widely used to optimize networks and support aerial platforms. Their construction requires location-labeled signal measurements from distributed users, raising fundamental concerns about location privacy. Even when raw data are kept local, the shared model updates can reveal user locations through their spatial structure, while naive noise injection either fails to hide this leakage or degrades model accuracy. This work analyzes how location leakage arises from gradients in a virtual-environment radio map model and proposes a geometry-aligned differential privacy mechanism with heterogeneous noise tailored to both confuse localization and cover gradient spatial patterns. The approach is theoretically supported with a convergence guarantee linking privacy strength to learning accuracy. Numerical experiments show…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Indoor and Outdoor Localization Technologies · UAV Applications and Optimization
