Echoes of Privacy: Uncovering the Profiling Practices of Voice Assistants

Tina Khezresmaeilzadeh, Elaine Zhu, Kiersten Grieco, Daniel J. Dubois, Konstantinos Psounis, David Choffnes
{"title":"Echoes of Privacy: Uncovering the Profiling Practices of Voice Assistants","authors":"Tina Khezresmaeilzadeh, Elaine Zhu, Kiersten Grieco, Daniel J. Dubois, Konstantinos Psounis, David Choffnes","doi":"arxiv-2409.07444","DOIUrl":null,"url":null,"abstract":"Many companies, including Google, Amazon, and Apple, offer voice assistants\nas a convenient solution for answering general voice queries and accessing\ntheir services. These voice assistants have gained popularity and can be easily\naccessed through various smart devices such as smartphones, smart speakers,\nsmartwatches, and an increasing array of other devices. However, this\nconvenience comes with potential privacy risks. For instance, while companies\nvaguely mention in their privacy policies that they may use voice interactions\nfor user profiling, it remains unclear to what extent this profiling occurs and\nwhether voice interactions pose greater privacy risks compared to other\ninteraction modalities. In this paper, we conduct 1171 experiments involving a total of 24530 queries\nwith different personas and interaction modalities over the course of 20 months\nto characterize how the three most popular voice assistants profile their\nusers. We analyze factors such as the labels assigned to users, their accuracy,\nthe time taken to assign these labels, differences between voice and web\ninteractions, and the effectiveness of profiling remediation tools offered by\neach voice assistant. Our findings reveal that profiling can happen without\ninteraction, can be incorrect and inconsistent at times, may take several days\nto weeks for changes to occur, and can be influenced by the interaction\nmodality.","PeriodicalId":501280,"journal":{"name":"arXiv - CS - Networking and Internet Architecture","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Networking and Internet Architecture","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.07444","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Many companies, including Google, Amazon, and Apple, offer voice assistants as a convenient solution for answering general voice queries and accessing their services. These voice assistants have gained popularity and can be easily accessed through various smart devices such as smartphones, smart speakers, smartwatches, and an increasing array of other devices. However, this convenience comes with potential privacy risks. For instance, while companies vaguely mention in their privacy policies that they may use voice interactions for user profiling, it remains unclear to what extent this profiling occurs and whether voice interactions pose greater privacy risks compared to other interaction modalities. In this paper, we conduct 1171 experiments involving a total of 24530 queries with different personas and interaction modalities over the course of 20 months to characterize how the three most popular voice assistants profile their users. We analyze factors such as the labels assigned to users, their accuracy, the time taken to assign these labels, differences between voice and web interactions, and the effectiveness of profiling remediation tools offered by each voice assistant. Our findings reveal that profiling can happen without interaction, can be incorrect and inconsistent at times, may take several days to weeks for changes to occur, and can be influenced by the interaction modality.
隐私的回声揭秘语音助手的貌相做法
包括谷歌、亚马逊和苹果在内的许多公司都提供语音助手,作为回答一般语音询问和访问其服务的便捷解决方案。这些语音助手越来越受欢迎,可以通过各种智能设备轻松访问,如智能手机、智能扬声器、智能手表和越来越多的其他设备。然而,这种便利也带来了潜在的隐私风险。例如,虽然公司在隐私政策中含糊地提到他们可能会使用语音交互进行用户分析,但这种分析在多大程度上会发生,以及语音交互与其他交互方式相比是否会带来更大的隐私风险,目前仍不清楚。在本文中,我们进行了 1171 次实验,在 20 个月的时间里使用不同的角色和交互模式共进行了 24530 次查询,以描述三种最流行的语音助手是如何对用户进行特征分析的。我们分析了分配给用户的标签、标签的准确性、分配这些标签所需的时间、语音交互和网络交互之间的差异以及每个语音助手提供的剖析补救工具的有效性等因素。我们的研究结果表明,分析可能在没有交互的情况下进行,有时可能不正确和不一致,可能需要几天到几周的时间才能发生变化,而且可能受到交互模式的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信