{"title":"苹果健康数据的来源:更新历史年表","authors":"Luke Jennings , Matthew Sorell , Hugo G. Espinosa","doi":"10.1016/j.fsidi.2024.301804","DOIUrl":null,"url":null,"abstract":"<div><div>Fitness tracking smart watches are becoming more prevalent in investigations and the need to understand and document their forensic potential and limitations is important for practitioners and researchers. Such fitness devices have undergone several hardware and software upgrades, changing the way they operate and evolving as more sophisticated pieces of technology. One example is the Apple Watch, working in conjunction with the Apple iPhone, to measure and record a vast amount of health information in the Apple Health database, <em>healthdb</em>_<em>secure</em>.<em>sqlite</em>. Over time, an end user will update their devices, but their health data, uniquely, carries over from one device to the next. In this paper, we investigate and analyse the hardware and software provenance of a real 5+ year Apple Health dataset to determine changes, patterns and anomalies over time. This provenance investigation provides insights in the form of (1) a timeline, representing the dataset's history of device and firmware updates that can be used in the context of investigation validation, (2) anomaly detection and, (3) insights into cyber hygiene. Analysis of the non-health data recorded in the health database arguably provides just as much insightful information as the health data itself.</div></div>","PeriodicalId":48481,"journal":{"name":"Forensic Science International-Digital Investigation","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"The provenance of Apple Health data: A timeline of update history\",\"authors\":\"Luke Jennings , Matthew Sorell , Hugo G. Espinosa\",\"doi\":\"10.1016/j.fsidi.2024.301804\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Fitness tracking smart watches are becoming more prevalent in investigations and the need to understand and document their forensic potential and limitations is important for practitioners and researchers. Such fitness devices have undergone several hardware and software upgrades, changing the way they operate and evolving as more sophisticated pieces of technology. One example is the Apple Watch, working in conjunction with the Apple iPhone, to measure and record a vast amount of health information in the Apple Health database, <em>healthdb</em>_<em>secure</em>.<em>sqlite</em>. Over time, an end user will update their devices, but their health data, uniquely, carries over from one device to the next. In this paper, we investigate and analyse the hardware and software provenance of a real 5+ year Apple Health dataset to determine changes, patterns and anomalies over time. This provenance investigation provides insights in the form of (1) a timeline, representing the dataset's history of device and firmware updates that can be used in the context of investigation validation, (2) anomaly detection and, (3) insights into cyber hygiene. Analysis of the non-health data recorded in the health database arguably provides just as much insightful information as the health data itself.</div></div>\",\"PeriodicalId\":48481,\"journal\":{\"name\":\"Forensic Science International-Digital Investigation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-10-01\",\"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/S2666281724001288\",\"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}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Forensic Science International-Digital Investigation","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666281724001288","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
摘要
健身追踪智能手表在调查中越来越普遍,对于从业人员和研究人员来说,了解和记录其取证潜力和局限性非常重要。此类健身设备经历了多次硬件和软件升级,改变了其操作方式,并发展成为更先进的技术。其中一个例子是 Apple Watch,它与 Apple iPhone 配合使用,可测量大量健康信息并将其记录到 Apple Health 数据库 healthdb_secure.sqlite。随着时间的推移,终端用户会更新他们的设备,但他们的健康数据会从一台设备唯一地延续到下一台设备。在本文中,我们调查并分析了一个真实的 5 年以上 Apple Health 数据集的硬件和软件出处,以确定随时间推移出现的变化、模式和异常。这种出处调查提供了以下形式的见解:(1) 时间轴,代表数据集的设备和固件更新历史,可用于调查验证;(2) 异常检测;(3) 网络卫生见解。可以说,对健康数据库中记录的非健康数据进行分析,与健康数据本身一样能提供具有洞察力的信息。
The provenance of Apple Health data: A timeline of update history
Fitness tracking smart watches are becoming more prevalent in investigations and the need to understand and document their forensic potential and limitations is important for practitioners and researchers. Such fitness devices have undergone several hardware and software upgrades, changing the way they operate and evolving as more sophisticated pieces of technology. One example is the Apple Watch, working in conjunction with the Apple iPhone, to measure and record a vast amount of health information in the Apple Health database, healthdb_secure.sqlite. Over time, an end user will update their devices, but their health data, uniquely, carries over from one device to the next. In this paper, we investigate and analyse the hardware and software provenance of a real 5+ year Apple Health dataset to determine changes, patterns and anomalies over time. This provenance investigation provides insights in the form of (1) a timeline, representing the dataset's history of device and firmware updates that can be used in the context of investigation validation, (2) anomaly detection and, (3) insights into cyber hygiene. Analysis of the non-health data recorded in the health database arguably provides just as much insightful information as the health data itself.