CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems

• URL: http://arxiv.org/abs/2508.02553v1
• Date: Mon, 04 Aug 2025 16:04:20 GMT
• Title: CSI Obfuscation: Single-Antenna Transmitters Can Not Hide from Adversarial Multi-Antenna Radio Localization Systems
• Authors: Phillip Stephan, Florian Euchner, Stephan ten Brink,
• Abstract summary: Single-antenna transmitters can obfuscate the signal by convolving it with a randomized sequence prior to transmission.<n>This strategy is only effective against CSI-based localization systems deploying single-antenna receivers.<n>Inspired by the concept of blind multichannel identification, we propose a simple CSI recovery method for multi-antenna receivers.
• Score: 5.881727527356694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of modern telecommunication systems to locate users and objects in the radio environment raises justified privacy concerns. To prevent unauthorized localization, single-antenna transmitters can obfuscate the signal by convolving it with a randomized sequence prior to transmission, which alters the channel state information (CSI) estimated at the receiver. However, this strategy is only effective against CSI-based localization systems deploying single-antenna receivers. Inspired by the concept of blind multichannel identification, we propose a simple CSI recovery method for multi-antenna receivers to extract channel features that ensure reliable user localization regardless of the transmitted signal. We comparatively evaluate the impact of signal obfuscation and the proposed recovery method on the localization performance of CSI fingerprinting, channel charting, and classical triangulation using real-world channel measurements. This work aims to demonstrate the necessity for further efforts to protect the location privacy of users from adversarial radio-based localization systems.

Related papers

• Enhancing Privacy in Semantic Communication over Wiretap Channels leveraging Differential Privacy [51.028047763426265]
  Semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information.<n> transmitting semantic-rich data over insecure channels introduces privacy risks.<n>This paper proposes a novel SemCom framework that integrates differential privacy mechanisms to protect sensitive semantic features.
  arXiv  Detail & Related papers  (2025-04-23T08:42:44Z)
• Secure Semantic Communication via Paired Adversarial Residual Networks [59.468221305630784]
  This letter explores the positive side of the adversarial attack for the security-aware semantic communication system. A pair of matching pluggable modules is installed: one after the semantic transmitter and the other before the semantic receiver. The proposed scheme is capable of fooling the eavesdropper while maintaining the high-quality semantic communication.
  arXiv  Detail & Related papers  (2024-07-02T08:32:20Z)
• Rethinking Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising [94.09442506816724]
  Blind-spot networks (BSN) have been prevalent neural architectures in self-supervised image denoising (SSID)<n>We build a Transformer-based Blind-Spot Network (TBSN) which shows strong local fitting and global perspective abilities.
  arXiv  Detail & Related papers  (2024-04-11T15:39:10Z)
• SCTransNet: Spatial-channel Cross Transformer Network for Infrared Small Target Detection [46.049401912285134]
  Infrared small target detection (IRSTD) has recently benefitted greatly from U-shaped neural models. Existing techniques struggle when the target has high similarities with the background. We present a Spatial-channel Cross Transformer Network (SCTransNet) that leverages spatial-channel cross transformer blocks.
  arXiv  Detail & Related papers  (2024-01-28T06:41:15Z)
• Privacy-Aware Joint Source-Channel Coding for image transmission based on Disentangled Information Bottleneck [27.929075969353764]
  Current privacy-aware joint source-channel coding (JSCC) works aim at avoiding private information transmission by adversarially training the J SCC encoder and decoder. We propose a novel privacy-aware J SCC based on disentangled information bottleneck (DIB-PAJSCC) We show that DIB-PAJSCC can reduce the eavesdropping accuracy on private information by up to 20% compared to existing methods.
  arXiv  Detail & Related papers  (2023-09-15T06:34:22Z)
• Secure Deep-JSCC Against Multiple Eavesdroppers [13.422085141752468]
  We propose an end-to-end (E2E) learning-based approach for secure communication against multiple eavesdroppers. We implement deep neural networks (DNNs) to realize a data-driven secure communication scheme. Our experiments show that employing the proposed secure neural encoding can decrease the adversarial accuracy by 28%.
  arXiv  Detail & Related papers  (2023-08-05T14:40:35Z)
• Is Semantic Communications Secure? A Tale of Multi-Domain Adversarial Attacks [70.51799606279883]
  We introduce test-time adversarial attacks on deep neural networks (DNNs) for semantic communications. We show that it is possible to change the semantics of the transferred information even when the reconstruction loss remains low.
  arXiv  Detail & Related papers  (2022-12-20T17:13:22Z)
• Spoofing Attack Detection in the Physical Layer with Commutative Neural Networks [21.6399273864521]
  In a spoofing attack, an attacker impersonates a legitimate user to access or tamper with data intended for or produced by the legitimate user. Existing schemes rely on long-term estimates, which makes it difficult to distinguish spoofing from movement of a legitimate user. This limitation is here addressed by means of a deep neural network that implicitly learns the distribution of pairs of short-term RSS vector estimates.
  arXiv  Detail & Related papers  (2022-11-08T14:20:58Z)
• Covert Communications via Adversarial Machine Learning and Reconfigurable Intelligent Surfaces [46.34482158291128]
  The reconfigurable intelligent surfaces (RISs) rely on arrays of unit cells to control the scattering and reflection profiles of signals. In this paper, covert communication is considered in the presence of the RIS.
  arXiv  Detail & Related papers  (2021-12-21T18:23:57Z)
• FedRec: Federated Learning of Universal Receivers over Fading Channels [92.15358738530037]
  We propose a neural network-based symbol detection technique for downlink fading channels. Multiple users collaborate to jointly learn a universal data-driven detector, hence the name FedRec. The performance of the resulting receiver is shown to approach the MAP performance in diverse channel conditions without requiring knowledge of the fading statistics.
  arXiv  Detail & Related papers  (2020-11-14T11:29:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.