GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction

• URL: http://arxiv.org/abs/2607.03254v1
• Date: Fri, 03 Jul 2026 12:14:52 GMT
• Title: GDPR-Aware Trajectory Sharing for ISAC-Assisted Robot Navigation: A Case Study on FID-Constrained Collision Prediction
• Abstract summary: FID-controlled sharing achieves strictly better privacy-utility tradeoff than fixed-error traces matched missed-conflict rates.<n>Minimisation of technical leakage establishes information-aware perturbation measure.<n>Experiments show that FID-controlled sharing achieves a strictly better privacy-utility tradeoff than fixed-error traces matched missed-conflict rates.
• Score: 29.756202665393943
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Integrated sensing and communication (ISAC) enables intelligent wireless infrastructure but raises growing regulatory concern as fine-grained personal trajectory histories become a byproduct of sensing. General Data Protection Regulation (GDPR) Articles 5(1)(c) and 5(1)(f) require that personal data be limited to what is necessary and protected through appropriate technical measures against unauthorised reconstruction. This paper addresses both requirements through a Fisher information density (FID)-constrained trajectory sharing scheme for robot collision avoidance, where sensing estimates are perturbed according to local information content before sharing. Experiments on real pedestrian traces show that FID-controlled sharing achieves a strictly better privacy-utility tradeoff than fixed-error perturbation: at matched missed-conflict rates, reconstruction leakage and sustained exposure lengths are consistently lower, establishing information-aware perturbation as a principled technical measure aligned with GDPR data minimisation and integrity requirements.

Related papers

• AmbSentry: Mitigating Sensing Eavesdropping in ISAC Systems by Harnessing Ambient IoT Devices [60.083979780396014]
  Integrated sensing and communication (ISAC) has emerged as a pivotal paradigm for 6G networks.<n>This paper proposes AmbSentry, an ISAC system that prevents the leakage of sensing information to sensing eavesdroppers.
  arXiv  Detail & Related papers  (2026-08-12T08:42:05Z)
• SP2UBI: Secure and Privacy-Preserving Usage-Based Insurance [0.0]
  We propose SP2UBI, a privacy-preserving UBI solution which guarantees mutual confidentiality.<n>By utilizing the Torus Fully Homomorphic Encryption scheme, computations are performed directly over encrypted data.<n>In order to protect data integrity against sensor level manipulation, SP2UBI incorporates a speed verification system assisted by Integrated Sensing and Communications (ISAC) technology.
  arXiv  Detail & Related papers  (2026-08-02T23:20:15Z)
• GDPR-Compliant Person Recognition in Industrial Environments Using MEMS-LiDAR and Hybrid Data [0.0]
  This paper presents a privacy-compliant approach based on Micro-Electro-Mechanical Systems LiDAR (MEMS-LiDAR)<n>MEMS-LiDAR exclusively captures anonymized 3D point clouds and avoids personal identification features.<n>Results demonstrate that the hybrid data improves the average precision by 44 percentage points compared to a model trained exclusively with real data.
  arXiv  Detail & Related papers  (2026-02-02T07:48:03Z)
• SAFE: Secure and Accurate Federated Learning for Privacy-Preserving Brain-Computer Interfaces [9.411011131466426]
  This paper proposes SAFE, a federated learning-based approach that protects user privacy by keeping data local during model training.<n>SAFE consistently outperformed 14 state-of-the-art approaches in both decoding accuracy and adversarial robustness, while ensuring privacy protection.<n>To our knowledge, SAFE is the first algorithm to simultaneously achieve high decoding accuracy, strong adversarial robustness, and reliable privacy protection without using any calibration data from the target subject.
  arXiv  Detail & Related papers  (2026-01-09T13:29:41Z)
• Adversary-Aware Private Inference over Wireless Channels [51.93574339176914]
  AI-based sensing at wireless edge devices has the potential to significantly enhance Artificial Intelligence (AI) applications.<n>As sensitive personal data can be reconstructed by an adversary, transformation of the features are required to reduce the risk of privacy violations.<n>We propose a novel framework for privacy-preserving AI-based sensing, where devices apply transformations of extracted features before transmission to a model server.
  arXiv  Detail & Related papers  (2025-10-23T13:02:14Z)
• Differential Privacy for Regulatory Compliance in Cyberattack Detection on Critical Infrastructure Systems [0.0]
  This paper presents a cyberattack detection framework geared towards enhancing regulatory confidence while alleviating privacy concerns of CIN stakeholders.<n>We show that our method induces a misclassification error rate comparable to the non-DP cases while delivering robust privacy guarantees.
  arXiv  Detail & Related papers  (2025-08-11T17:10:49Z)
• Context-Aware Online Conformal Anomaly Detection with Prediction-Powered Data Acquisition [35.59201763567714]
  We introduce context-aware prediction-powered conformal online anomaly detection (C-PP-COAD)<n>Our framework strategically leverages synthetic calibration data to mitigate data scarcity, while adaptively integrating real data based on contextual cues.<n>Experiments conducted on both synthetic and real-world datasets demonstrate that C-PP-COAD significantly reduces dependency on real calibration data without compromising guaranteed false discovery rate (FDR)
  arXiv  Detail & Related papers  (2025-05-03T10:58:05Z)
• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG)<n>FedE4RAG facilitates collaborative training of client-side RAG retrieval models.<n>We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• Communication-Efficient and Privacy-Adaptable Mechanism for Federated Learning [54.20871516148981]
  We introduce the Communication-Efficient and Privacy-Adaptable Mechanism (CEPAM)<n>CEPAM achieves communication efficiency and privacy protection simultaneously.<n>We theoretically analyze the privacy guarantee of CEPAM and investigate the trade-offs among user privacy and accuracy of CEPAM.
  arXiv  Detail & Related papers  (2025-01-21T11:16:05Z)
• The Data Minimization Principle in Machine Learning [61.17813282782266]
  Data minimization aims to reduce the amount of data collected, processed or retained. It has been endorsed by various global data protection regulations. However, its practical implementation remains a challenge due to the lack of a rigorous formulation.
  arXiv  Detail & Related papers  (2024-05-29T19:40:27Z)
• Distributed Machine Learning and the Semblance of Trust [66.1227776348216]
  Federated Learning (FL) allows the data owner to maintain data governance and perform model training locally without having to share their data. FL and related techniques are often described as privacy-preserving. We explain why this term is not appropriate and outline the risks associated with over-reliance on protocols that were not designed with formal definitions of privacy in mind.
  arXiv  Detail & Related papers  (2021-12-21T08:44:05Z)
• Privacy Preservation in Federated Learning: An insightful survey from the GDPR Perspective [10.901568085406753]
  Article is dedicated to surveying on the state-of-the-art privacy techniques, which can be employed in Federated learning. Recent research has demonstrated that retaining data and on computation in FL is not enough for privacy-guarantee. This is because ML model parameters exchanged between parties in an FL system, which can be exploited in some privacy attacks.
  arXiv  Detail & Related papers  (2020-11-10T21:41:25Z)
• Privacy-preserving Traffic Flow Prediction: A Federated Learning Approach [61.64006416975458]
  We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction. FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism. It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
  arXiv  Detail & Related papers  (2020-03-19T13:07:49Z)

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.