{"title":"I know where you have been last summer: Extracting privacy-sensitive information via forensic analysis of the Mercedes-Benz NTG5*2 infotainment system","authors":"Dario Stabili, Filip Valgimigli, Mirco Marchetti","doi":"10.1016/j.fsidi.2025.301909","DOIUrl":null,"url":null,"abstract":"<div><div>Modern vehicles are equipped with In-Vehicle Infotainment (IVI) systems that offers different functions, such as typical radio and multimedia services, navigation and internet browsing. To operate properly, IVI systems have to store locally different types of data, reflecting user preferences and behaviors. If stored and managed insecurely, these data might expose sensitive information and represent a privacy risk. In this paper we address this issue by presenting a methodology for the extraction of privacy-sensitive information from the popular <span><math><mi>N</mi><mi>T</mi><mi>G</mi><mn>5</mn></math></span> COMMAND IVI system (specifically, the <span><math><mi>N</mi><mi>T</mi><mi>G</mi><mn>5</mn><mo>⁎</mo><mn>2</mn></math></span> version by Harman), deployed in some Mercedes-Benz vehicles from 2013 to 2019. We show that it is possible to extract information related to geographic locations and various vehicles events (such as ignition and doors opening and closing) dating back to the previous 8 months, and that these data can be cross-referenced to precisely identify the activities and habits of the driver. Moreover, we develop a novel forensic tool to automate this task.<span><span><sup>1</sup></span></span> Given the past usage of the <span><math><mi>N</mi><mi>T</mi><mi>G</mi><mn>5</mn></math></span> system, our work might have real life implications for the privacy of millions of drivers, owners and passengers. As a final contribution, we develop a novel technique for SQLite data carving specifically designed to identify deleted data. Comparison with existing state-of-the-art tools for SQLite3 data recovery demonstrates that our approach is more effective in recovering deleted traces than general purpose tools.</div></div>","PeriodicalId":48481,"journal":{"name":"Forensic Science International-Digital Investigation","volume":"53 ","pages":"Article 301909"},"PeriodicalIF":2.0000,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic Science International-Digital Investigation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666281725000484","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Modern vehicles are equipped with In-Vehicle Infotainment (IVI) systems that offers different functions, such as typical radio and multimedia services, navigation and internet browsing. To operate properly, IVI systems have to store locally different types of data, reflecting user preferences and behaviors. If stored and managed insecurely, these data might expose sensitive information and represent a privacy risk. In this paper we address this issue by presenting a methodology for the extraction of privacy-sensitive information from the popular COMMAND IVI system (specifically, the version by Harman), deployed in some Mercedes-Benz vehicles from 2013 to 2019. We show that it is possible to extract information related to geographic locations and various vehicles events (such as ignition and doors opening and closing) dating back to the previous 8 months, and that these data can be cross-referenced to precisely identify the activities and habits of the driver. Moreover, we develop a novel forensic tool to automate this task.1 Given the past usage of the system, our work might have real life implications for the privacy of millions of drivers, owners and passengers. As a final contribution, we develop a novel technique for SQLite data carving specifically designed to identify deleted data. Comparison with existing state-of-the-art tools for SQLite3 data recovery demonstrates that our approach is more effective in recovering deleted traces than general purpose tools.