{"title":"使用匿名WiFi接入点BSSID确定位置和移动模式","authors":"M. N. Sakib, Junaed Bin Halim, Chin-Tser Huang","doi":"10.1109/SECTECH.2014.10","DOIUrl":null,"url":null,"abstract":"Location tracking applications are usually based on exact GPS location information and non-anonymized WiFi Access Point (AP) information. It has been assumed that anonymizing WiFi AP information is an effective way to preserve location privacy of the connected users. In this work, our goal is to show that the privacy of connected users' movement pattern can still be compromised by a determined attacker even if the WiFi AP information is anonymized. We investigated the feasibility of tracking user's movement between locations from anonymized WiFi AP BSSIDs based on large Device Analyzer datasets. Our experiments show that the user's daily movement pattern can be identified with almost 83% accuracy.","PeriodicalId":159028,"journal":{"name":"2014 7th International Conference on Security Technology","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Determining Location and Movement Pattern Using Anonymized WiFi Access Point BSSID\",\"authors\":\"M. N. Sakib, Junaed Bin Halim, Chin-Tser Huang\",\"doi\":\"10.1109/SECTECH.2014.10\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Location tracking applications are usually based on exact GPS location information and non-anonymized WiFi Access Point (AP) information. It has been assumed that anonymizing WiFi AP information is an effective way to preserve location privacy of the connected users. In this work, our goal is to show that the privacy of connected users' movement pattern can still be compromised by a determined attacker even if the WiFi AP information is anonymized. We investigated the feasibility of tracking user's movement between locations from anonymized WiFi AP BSSIDs based on large Device Analyzer datasets. Our experiments show that the user's daily movement pattern can be identified with almost 83% accuracy.\",\"PeriodicalId\":159028,\"journal\":{\"name\":\"2014 7th International Conference on Security Technology\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-12-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2014 7th International Conference on Security Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SECTECH.2014.10\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 7th International Conference on Security Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SECTECH.2014.10","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
摘要
位置跟踪应用程序通常基于精确的GPS位置信息和非匿名的WiFi接入点(AP)信息。人们一直认为匿名化WiFi AP信息是保护连接用户位置隐私的有效方法。在这项工作中,我们的目标是表明,即使WiFi AP信息是匿名的,连接用户的移动模式的隐私仍然可以被确定的攻击者破坏。我们研究了基于大型设备分析器数据集的匿名WiFi AP bssid跟踪用户在不同位置之间移动的可行性。我们的实验表明,用户的日常运动模式可以识别几乎83%的准确率。
Determining Location and Movement Pattern Using Anonymized WiFi Access Point BSSID
Location tracking applications are usually based on exact GPS location information and non-anonymized WiFi Access Point (AP) information. It has been assumed that anonymizing WiFi AP information is an effective way to preserve location privacy of the connected users. In this work, our goal is to show that the privacy of connected users' movement pattern can still be compromised by a determined attacker even if the WiFi AP information is anonymized. We investigated the feasibility of tracking user's movement between locations from anonymized WiFi AP BSSIDs based on large Device Analyzer datasets. Our experiments show that the user's daily movement pattern can be identified with almost 83% accuracy.