HideOut - User Protection Against Localization with Throughput Guarantees in Mobile Networks

Abstract

Channel state information (CSI) in cellular networks is primarily used to maintain quality of service (QoS), yet it leaks privacy-sensitive information, in particular the user equipment's (UE) location. CSI is reported in plaintext on a publicly available schedule; thus, capturing it on the air and using a well trained AI model to localize the UE is straightforward. This simplicity in the attack execution makes it a serious privacy vulnerability. Using conventional privacy enhancing techniques, such as additive perturbation from differential privacy, to protect location data can potentially degrade the downlink communication quality. We present HideOut, a client-side defense mechanism that preserves the user's location from neural-based localization methods, while maintaining their downlink Spectral Efficiency (SE). Unlike generic adversarial attacks, HideOut treats downlink SE as a strict constraint in an optimization setting. Under maximum ratio transmission (MRT) beamforming, we show that a user-selected SE loss budget can be converted into an angular rate-feasible constraint on the reported channel vector. HideOut then performs gradient-based localization obfuscation while projecting each update onto this rate-feasible set. We evaluate HideOut against four CSI-based localization architectures under both white-box and black-box assumptions. Across models, HideOut increases localization ambiguity (error) from approximately $1--2$ m to $72--85$ m while keeping downlink SE loss within a $1\%$ budget. These results show that CSI-based localization can be substantially degraded at negligible communication quality loss when adversarial perturbations are made aware of the wireless link.