违背(适当)流程:隐私政策分析的上下文完整性方法

Yan Shvartzshnaider, Noah J. Apthorpe, N. Feamster, H. Nissenbaum
{"title":"违背(适当)流程:隐私政策分析的上下文完整性方法","authors":"Yan Shvartzshnaider, Noah J. Apthorpe, N. Feamster, H. Nissenbaum","doi":"10.1609/hcomp.v7i1.5266","DOIUrl":null,"url":null,"abstract":"We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.","PeriodicalId":87339,"journal":{"name":"Proceedings of the ... AAAI Conference on Human Computation and Crowdsourcing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"27","resultStr":"{\"title\":\"Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis\",\"authors\":\"Yan Shvartzshnaider, Noah J. Apthorpe, N. Feamster, H. Nissenbaum\",\"doi\":\"10.1609/hcomp.v7i1.5266\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.\",\"PeriodicalId\":87339,\"journal\":{\"name\":\"Proceedings of the ... AAAI Conference on Human Computation and Crowdsourcing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-10-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"27\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the ... AAAI Conference on Human Computation and Crowdsourcing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1609/hcomp.v7i1.5266\",\"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 ... AAAI Conference on Human Computation and Crowdsourcing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/hcomp.v7i1.5266","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 27

摘要

我们提出了一种使用上下文完整性(CI)框架分析隐私策略的方法。这种方法允许对阻碍读者理解和评估公司数据收集实践能力的隐私政策声明问题进行系统化检测。这些问题包括缺少上下文细节、语言模糊以及对所描述的信息传输的压倒性可能的解释。我们在两种不同的设置中演示这种方法。首先,我们比较了剑桥分析公司丑闻前后Facebook的隐私政策版本。我们的分析表明,更新后的政策仍然存在根本性的模糊性,限制了读者对Facebook数据收集实践的理解。其次,我们成功地众包了来自17家公司141名众包工作者的48个隐私政策摘录的CI注释。这表明普通用户能够可靠地识别隐私政策声明中的上下文信息,众包可以帮助我们将CI分析方法扩展到更多的隐私政策声明中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Going against the (Appropriate) Flow: A Contextual Integrity Approach to Privacy Policy Analysis
We present a method for analyzing privacy policies using the framework of contextual integrity (CI). This method allows for the systematized detection of issues with privacy policy statements that hinder readers’ ability to understand and evaluate company data collection practices. These issues include missing contextual details, vague language, and overwhelming possible interpretations of described information transfers. We demonstrate this method in two different settings. First, we compare versions of Facebook’s privacy policy from before and after the Cambridge Analytica scandal. Our analysis indicates that the updated policy still contains fundamental ambiguities that limit readers’ comprehension of Facebook’s data collection practices. Second, we successfully crowdsourced CI annotations of 48 excerpts of privacy policies from 17 companies with 141 crowdworkers. This indicates that regular users are able to reliably identify contextual information in privacy policy statements and that crowdsourcing can help scale our CI analysis method to a larger number of privacy policy statements.
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
×
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