Game-Theoretic Framework for Private Data Sharing in Vehicular Networks

• URL: http://arxiv.org/abs/2606.22115v1
• Date: Sat, 20 Jun 2026 15:54:44 GMT
• Title: Game-Theoretic Framework for Private Data Sharing in Vehicular Networks
• Abstract summary: We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems.<n>Our framework ensures that only the data consumer can access the fully aggregated data.<n>We empirically validate our framework using real-world vehicular location data.
• Score: 5.871863362316698
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems. The proposed hybrid architecture comprises vehicles that supply sensor data, independent servers that process data via secure multiparty computation, a coordinator node that manages data flow, and data consumers that set economic incentives. Crucially, our framework ensures that only the data consumer can access the fully aggregated data, preventing individual raw data exposure and significantly reducing privacy risks. By integrating principles of the Stackelberg competition from game theory, our approach dynamically balances privacy and economic incentives, enabling vehicles to make participation decisions based on perceived privacy risks and incentives. We empirically validate our framework using real-world vehicular location data, quantifying privacy risks by evaluating the accuracy with which a potential adversary can reconstruct a vehicle's path using only a subset of the shared data. This paper details the development and deployment of a data-trading platform within this framework, introducing a practical and privacy-preserving marketplace for profitable vehicle data sharing. Through experiments and simulations, we evaluate the effectiveness of the system in preserving privacy and explore the dynamics that influence vehicle participation. Our findings highlight the robustness of the proposed framework in preserving privacy while supporting an active data market.

Related papers

• How Safe Is Your Data in Connected and Autonomous Cars: A Consumer Advantage or a Privacy Nightmare ? [21.526036185120287]
  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.
  arXiv  Detail & Related papers  (2026-01-18T06:45:21Z)
• Privacy-Preserving Dataset Combination [1.0485433579460999]
  We introduce SecureKL, a protocol for dataset-to-dataset evaluations with zero privacy leakage.<n>SecureKL evaluates a source dataset against candidates, performing dataset divergence metrics internally with private computations.<n>On real-world data, SecureKL achieves high consistency ($>90%$ correlation with non-private counterparts) and successfully identifies beneficial data collaborations.
  arXiv  Detail & Related papers  (2025-02-09T03:54:17Z)
• 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)
• CaPS: Collaborative and Private Synthetic Data Generation from Distributed Sources [5.898893619901382]
  We propose a framework for the collaborative and private generation of synthetic data from distributed data holders. We replace the trusted aggregator with secure multi-party computation protocols and output privacy via differential privacy (DP) We demonstrate the applicability and scalability of our approach for the state-of-the-art select-measure-generate algorithms MWEM+PGM and AIM.
  arXiv  Detail & Related papers  (2024-02-13T17:26:32Z)
• Libertas: Privacy-Preserving Collective Computation for Decentralised Personal Data Stores [18.91869691495181]
  We introduce a modular architecture, Libertas, to integrate MPC with PDS like Solid.<n>We introduce a paradigm shift from an omniscient' view to individual-based, user-centric view of trust and security.
  arXiv  Detail & Related papers  (2023-09-28T12:07:40Z)
• A Unified View of Differentially Private Deep Generative Modeling [60.72161965018005]
  Data with privacy concerns comes with stringent regulations that frequently prohibited data access and data sharing. Overcoming these obstacles is key for technological progress in many real-world application scenarios that involve privacy sensitive data. Differentially private (DP) data publishing provides a compelling solution, where only a sanitized form of the data is publicly released.
  arXiv  Detail & Related papers  (2023-09-27T14:38:16Z)
• Blockchain-empowered Federated Learning for Healthcare Metaverses: User-centric Incentive Mechanism with Optimal Data Freshness [66.3982155172418]
  We first design a user-centric privacy-preserving framework based on decentralized Federated Learning (FL) for healthcare metaverses. We then utilize Age of Information (AoI) as an effective data-freshness metric and propose an AoI-based contract theory model under Prospect Theory (PT) to motivate sensing data sharing.
  arXiv  Detail & Related papers  (2023-07-29T12:54:03Z)
• 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)
• Lessons from the AdKDD'21 Privacy-Preserving ML Challenge [57.365745458033075]
  A prominent proposal at W3C only allows sharing advertising signals through aggregated, differentially private reports of past displays. To study this proposal extensively, an open Privacy-Preserving Machine Learning Challenge took place at AdKDD'21. A key finding is that learning models on large, aggregated data in the presence of a small set of unaggregated data points can be surprisingly efficient and cheap.
  arXiv  Detail & Related papers  (2022-01-31T11:09:59Z)
• Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy [1.911678487931003]
  We propose a general execution model to control the data-dependence of communications in user-side decentralized computations. Our formal privacy guarantees leverage and extend recent results on privacy amplification by shuffling.
  arXiv  Detail & Related papers  (2021-12-23T08:30:17Z)
• Data Sharing Markets [95.13209326119153]
  We study a setup where each agent can be both buyer and seller of data. We consider two cases: bilateral data exchange (trading data with data) and unilateral data exchange (trading data with money)
  arXiv  Detail & Related papers  (2021-07-19T06:00:34Z)

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.