解读广告主对智能手机用户隐私的看法

Yan Wang, Yingying Chen, Fan Ye, J. Yang, Hongbo Liu
{"title":"解读广告主对智能手机用户隐私的看法","authors":"Yan Wang, Yingying Chen, Fan Ye, J. Yang, Hongbo Liu","doi":"10.1109/ICDCS.2015.37","DOIUrl":null,"url":null,"abstract":"Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.","PeriodicalId":129182,"journal":{"name":"2015 IEEE 35th International Conference on Distributed Computing Systems","volume":"57 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Towards Understanding the Advertiser's Perspective of Smartphone User Privacy\",\"authors\":\"Yan Wang, Yingying Chen, Fan Ye, J. Yang, Hongbo Liu\",\"doi\":\"10.1109/ICDCS.2015.37\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.\",\"PeriodicalId\":129182,\"journal\":{\"name\":\"2015 IEEE 35th International Conference on Distributed Computing Systems\",\"volume\":\"57 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 IEEE 35th International Conference on Distributed Computing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICDCS.2015.37\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 35th International Conference on Distributed Computing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICDCS.2015.37","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 11

摘要

许多智能手机应用程序定期收集各种私人用户数据,并将其发送给广告商。尽管最近对保护机制和应用程序行为的分析进行了研究,但对这种隐私损失的后果的理解仍然有限。在本文中,我们研究了广告商通过结合来自多个应用程序和多个用户的数据可以推断出多少用户的社交和社区关系。在对200个最受欢迎的Android应用进行了一个月的用户研究后,我们发现广告商可以推断出90%的社交关系。我们进一步提出了一个隐私泄露推理框架,并使用真实的移动轨迹和Foursquare数据来量化隐私泄露的后果。我们发现,达到90%的社交和社区关系的推理准确率只需要3周的用户数据。这些发现强调了尽早采用隐私保护机制的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Towards Understanding the Advertiser's Perspective of Smartphone User Privacy
Many smartphone apps routinely gather various private user data and send them to advertisers. Despite recent study on protection mechanisms and analysis on apps' behavior, the understanding about the consequences of such privacy losses remains limited. In this paper we investigate how much an advertiser can infer about users' social and community relationships by combining data from multiple applications and across many users. After one month's user study involving about 200 most popular Android apps, we find that an advertiser can infer 90% of the social relationships. We further propose a privacy leakage inference framework and use real mobility traces and Foursquare data to quantify the consequences of privacy leakage. We find that achieving 90% inference accuracy of the social and community relationships requires merely 3 weeks' user data. The discoveries underscore the importance of early adoption of privacy protection mechanisms.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
FLOWPROPHET: Generic and Accurate Traffic Prediction for Data-Parallel Cluster Computing Improving the Energy Benefit for 802.3az Using Dynamic Coalescing Techniques Systematic Mining of Associated Server Herds for Malware Campaign Discovery Rain Bar: Robust Application-Driven Visual Communication Using Color Barcodes Optimizing Roadside Advertisement Dissemination in Vehicular Cyber-Physical Systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1