How Safe Is Your Data in Connected and Autonomous Cars: A Consumer Advantage or a Privacy Nightmare ?

• URL: http://arxiv.org/abs/2601.12284v1
• Date: Sun, 18 Jan 2026 06:45:21 GMT
• Title: How Safe Is Your Data in Connected and Autonomous Cars: A Consumer Advantage or a Privacy Nightmare ?
• Authors: Amit Chougule, Vinay Chamola, Norbert Herencsar, Fei Richard Yu,
• Abstract summary: Vehicle-to-Everything (V2X) communication enables autonomous cars to generate and exchange substantial amounts of data with real-world entities.<n>This review paper explores the multifaceted nature of data sharing in CAVs, analyzing its contributions to innovation and its associated vulnerabilities.<n>It emphasizes the urgent need for robust policies and ethical data management practices.
• Score: 21.526036185120287
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid evolution of the automobile sector, driven by advancements in connected and autonomous vehicles (CAVs), has transformed how vehicles communicate, operate, and interact with their surroundings. Technologies such as Vehicle-to-Everything (V2X) communication enable autonomous cars to generate and exchange substantial amounts of data with real-world entities, enhancing safety, improving performance, and delivering personalized user experiences. However, this data-driven ecosystem introduces significant challenges, particularly concerning data privacy, security, and governance. The absence of transparency and comprehensive regulatory frameworks exacerbates issues of unauthorized data access, prolonged retention, and potential misuse, creating tension between consumer benefits and privacy risks. This review paper explores the multifaceted nature of data sharing in CAVs, analyzing its contributions to innovation and its associated vulnerabilities. It evaluates data-sharing mechanisms and communication technologies, highlights the benefits of data exchange across various use cases, examines privacy concerns and risks of data misuse, and critically reviews regulatory frameworks and their inadequacies in safeguarding user privacy. By providing a thorough analysis of the current state of data sharing in the automotive sector, the paper emphasizes the urgent need for robust policies and ethical data management practices. It calls for striking a balance between fostering technological advancements and ensuring secure, consumer-friendly solutions, paving the way for a trustworthy and innovative automotive future.

Related papers

• 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)
• Connected and Exposed: Cybersecurity Risks, Regulatory Gaps, and Public Perception in Internet-Connected Vehicles [0.0]
  This paper explores the evolving threat landscape associated with connected vehicles.<n>It focuses on risks such as unauthorized remote access and the potential leakage of personal data.<n>To assess the current state of protection, we conducted a comprehensive analysis of 16 international standards and regulations.<n>We also carried out a user-focused survey designed to map consumer attitudes toward smart cars.
  arXiv  Detail & Related papers  (2025-08-21T06:51:54Z)
• Showcasing standards and approaches for cybersecurity, safety, and privacy issues in connected and autonomous vehicles [0.24475591916185502]
  This paper aims to bridge cybersecurity, safety, and privacy concerns in Connected and Autonomous Vehicles (CAV) with respect to Risk Assessment (RA) and Threat Modelling (TM)<n>We highlight latest standards and approaches in RA and TM research to tackle complex attack surfaces as the ones posed by automotive settings.
  arXiv  Detail & Related papers  (2025-08-02T05:45:50Z)
• GOLIATH: A Decentralized Framework for Data Collection in Intelligent Transportation Systems [9.535698390424669]
  GOLIATH is a decentralized framework that runs on the In-Vehicle Infotainment (IVI) system to collect real-time information exchanged between the network's participants.<n>We design the consensus mechanism resilient against a realistic set of adversaries that aim to tamper or disable the communication.
  arXiv  Detail & Related papers  (2025-06-12T12:55:25Z)
• Contextualizing Security and Privacy of Software-Defined Vehicles: State of the Art and Industry Perspectives [7.160802183553593]
  Survey explores the cybersecurity and privacy challenges posed by Software-Defined Vehicles (SDVs)<n>SDVs increasingly integrate features like Over-the-Air (OTA) updates and Vehicle-to-Everything (V2X) communication.<n>Transition to SDVs also raises significant privacy concerns, with vehicles collecting vast amounts of sensitive data.
  arXiv  Detail & Related papers  (2024-11-15T22:30:07Z)
• Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations [48.924085579865334]
  Analyzing human data is crucial for developing autonomous systems that replicate safe driving practices. This paper presents a comparative evaluation of human compliance with traffic and safety rules across multiple trajectory prediction datasets.
  arXiv  Detail & Related papers  (2024-11-04T09:21:00Z)
• Decentralized Multimedia Data Sharing in IoV: A Learning-based Equilibrium of Supply and Demand [57.82021900505197]
  Internet of Vehicles (IoV) has great potential to transform transportation systems by enhancing road safety, reducing traffic congestion, and improving user experience through onboard infotainment applications. Decentralized data sharing can improve security, privacy, reliability, and facilitate infotainment data sharing in IoVs. We propose a decentralized data-sharing incentive mechanism based on multi-intelligent reinforcement learning to learn the supply-demand balance in markets.
  arXiv  Detail & Related papers  (2024-03-29T14:58:28Z)
• Differentiated Security Architecture for Secure and Efficient Infotainment Data Communication in IoV Networks [55.340315838742015]
  Negligence on the security of infotainment data communication in IoV networks can unintentionally open an easy access point for social engineering attacks. In particular, we first classify data communication in the IoV network, examine the security focus of each data communication, and then develop a differentiated security architecture to provide security protection on a file-to-file basis.
  arXiv  Detail & Related papers  (2024-03-29T12:01:31Z)
• Auditing and Generating Synthetic Data with Controllable Trust Trade-offs [54.262044436203965]
  We introduce a holistic auditing framework that comprehensively evaluates synthetic datasets and AI models. It focuses on preventing bias and discrimination, ensures fidelity to the source data, assesses utility, robustness, and privacy preservation. We demonstrate the framework's effectiveness by auditing various generative models across diverse use cases.
  arXiv  Detail & Related papers  (2023-04-21T09:03:18Z)
• Situation Awareness for Autonomous Vehicles Using Blockchain-based Service Cooperation [4.1650513680603884]
  We propose a decentralized framework that enables smart contracts between traffic data producers and consumers based on blockchain. Autonomous vehicles connect to a local edge server, share their data, or use services based on agreements, for which the cooperating edge servers across the system provide a platform. Our results show that multicast transmissions in such a scenario boost the throughput up to 2.5 times where the data packets of different sizes can be transmitted in less than one second.
  arXiv  Detail & Related papers  (2022-04-07T09:23:30Z)
• Beyond privacy regulations: an ethical approach to data usage in transportation [64.86110095869176]
  We describe how Federated Machine Learning can be applied to the transportation sector. We see Federated Learning as a method that enables us to process privacy-sensitive data, while respecting customer's privacy.
  arXiv  Detail & Related papers  (2020-04-01T15:10:12Z)

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.