{"title":"应用程序提供商的轻量级用户跟踪方法","authors":"R. M. Frey, Runhua Xu, A. Ilic","doi":"10.1145/2903150.2903484","DOIUrl":null,"url":null,"abstract":"Since 2013, Google and Apple no longer allow app providers to use the persistent device identifiers (Android ID and UDID) for user tracking on mobile devices. Other tracking options provoke either severe privacy concerns, need additional hardware or are only practicable by a limited number of companies. In this paper, we present a lightweight method that overcomes these weaknesses by using the set of installed apps on a device to create a unique fingerprint. The method was evaluated in a field study with 2410 users and 175,658 installed apps in total. The sets of these installed apps are unique in 99.75% of all inspected users. Furthermore, by reducing the granularity from apps to app categories to lessen users' privacy concerns, the results remain highly unique with an identification rate of 96.22%. Since the information of installed apps and app categories on each device is freely available for any app developer, the method is a valuable instrument for app providers.","PeriodicalId":226569,"journal":{"name":"Proceedings of the ACM International Conference on Computing Frontiers","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"A lightweight user tracking method for app providers\",\"authors\":\"R. M. Frey, Runhua Xu, A. Ilic\",\"doi\":\"10.1145/2903150.2903484\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since 2013, Google and Apple no longer allow app providers to use the persistent device identifiers (Android ID and UDID) for user tracking on mobile devices. Other tracking options provoke either severe privacy concerns, need additional hardware or are only practicable by a limited number of companies. In this paper, we present a lightweight method that overcomes these weaknesses by using the set of installed apps on a device to create a unique fingerprint. The method was evaluated in a field study with 2410 users and 175,658 installed apps in total. The sets of these installed apps are unique in 99.75% of all inspected users. Furthermore, by reducing the granularity from apps to app categories to lessen users' privacy concerns, the results remain highly unique with an identification rate of 96.22%. Since the information of installed apps and app categories on each device is freely available for any app developer, the method is a valuable instrument for app providers.\",\"PeriodicalId\":226569,\"journal\":{\"name\":\"Proceedings of the ACM International Conference on Computing Frontiers\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-05-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ACM International Conference on Computing Frontiers\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2903150.2903484\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the ACM International Conference on Computing Frontiers","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2903150.2903484","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A lightweight user tracking method for app providers
Since 2013, Google and Apple no longer allow app providers to use the persistent device identifiers (Android ID and UDID) for user tracking on mobile devices. Other tracking options provoke either severe privacy concerns, need additional hardware or are only practicable by a limited number of companies. In this paper, we present a lightweight method that overcomes these weaknesses by using the set of installed apps on a device to create a unique fingerprint. The method was evaluated in a field study with 2410 users and 175,658 installed apps in total. The sets of these installed apps are unique in 99.75% of all inspected users. Furthermore, by reducing the granularity from apps to app categories to lessen users' privacy concerns, the results remain highly unique with an identification rate of 96.22%. Since the information of installed apps and app categories on each device is freely available for any app developer, the method is a valuable instrument for app providers.
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