关键词: 车联网, 大语言模型, 隐私合规, 流量分析, 隐私信息识别

Abstract: To address the privacy leakage risks existing in the application of Internet of Vehicles (IoV) devices, this paper systematically summarizes 359 types of IoV device privacy information classified into four categories by analyzing relevant IoV technical documents and academic papers, and constructs a corresponding privacy information classification system. Based on this classification system, this paper proposes a large language model (LLM)-based Vehicular Data-flow Privacy Compliance Analyzer (VDPAnalyzer) to conduct privacy compliance analysis on vehicular data streams. Based on the syntactic and semantic parsing of vehicular network traffic, the system integrates pattern matching and LLM technologies with prompt engineering and model fine-tuning strategies, so as to effectively detect privacy leakage problems and identify compliance risks in vehicular data streams. In this paper, network traffic data of 11 real-world IoV devices including on-board diagnostics (OBD) devices, vehicle-mounted cameras and in-vehicle infotainment systems are collected for experimental verification. The experimental results show that the fine-tuned LLM improves the precision of identifying privacy information in IoV traffic from 71.2% to 88.0%, and the recall rate from 85.7% to 86.3%. Among the 88 detected privacy leakage items, 32 items (accounting for 36.4%) are not stated in the devices privacy policies. More seriously, some IoV devices transmit captured video thumbnails and audio-to-text transcription results in unencrypted plaintext, causing severe risks of user personal privacy leakage.

Key words: internet of vehicles, large language models, privacy compliance, traffic analysis, privacy information identification