你的保单是否符合规定?:基于深度学习的隐私政策遵从GDPR的实证研究

Tamjid Al Rahat, Minjun Long, Yuan Tian
{"title":"你的保单是否符合规定?:基于深度学习的隐私政策遵从GDPR的实证研究","authors":"Tamjid Al Rahat, Minjun Long, Yuan Tian","doi":"10.1145/3559613.3563195","DOIUrl":null,"url":null,"abstract":"Since the General Data Protection Regulation (GDPR) came into force in May 2018, companies have worked on their data practices to comply with the requirements of GDPR. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy when using companies' services, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. In addition, our study shows that more than 32% of end users find it difficult to understand the privacy policies explaining GDPR requirements. Therefore, it is challenging for the end users and law enforcement authorities to manually check if companies' privacy policies comply with the requirements enforced by GDPR. In this paper, we create a privacy policy dataset of 1,080 websites annotated by experts with 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model that can classify the privacy policies into GDPR requirements with an accuracy of 89.2%. We apply our model to automatically measure GDPR compliance in the privacy policies of 9,761 most visited websites. Our results show that, even after four years since GDPR went into effect, 68% of websites still fail to comply with at least one requirement of GDPR.","PeriodicalId":416548,"journal":{"name":"Proceedings of the 21st Workshop on Privacy in the Electronic Society","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Is Your Policy Compliant?: A Deep Learning-based Empirical Study of Privacy Policies' Compliance with GDPR\",\"authors\":\"Tamjid Al Rahat, Minjun Long, Yuan Tian\",\"doi\":\"10.1145/3559613.3563195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Since the General Data Protection Regulation (GDPR) came into force in May 2018, companies have worked on their data practices to comply with the requirements of GDPR. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy when using companies' services, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. In addition, our study shows that more than 32% of end users find it difficult to understand the privacy policies explaining GDPR requirements. Therefore, it is challenging for the end users and law enforcement authorities to manually check if companies' privacy policies comply with the requirements enforced by GDPR. In this paper, we create a privacy policy dataset of 1,080 websites annotated by experts with 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model that can classify the privacy policies into GDPR requirements with an accuracy of 89.2%. We apply our model to automatically measure GDPR compliance in the privacy policies of 9,761 most visited websites. Our results show that, even after four years since GDPR went into effect, 68% of websites still fail to comply with at least one requirement of GDPR.\",\"PeriodicalId\":416548,\"journal\":{\"name\":\"Proceedings of the 21st Workshop on Privacy in the Electronic Society\",\"volume\":\"27 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 21st Workshop on Privacy in the Electronic Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3559613.3563195\",\"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 21st Workshop on Privacy in the Electronic Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3559613.3563195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

自《通用数据保护条例》(GDPR)于2018年5月生效以来,公司一直致力于其数据实践,以遵守GDPR的要求。特别是,由于隐私政策是用户在使用公司服务时了解和控制其隐私的重要沟通渠道,许多公司在GDPR实施后更新了隐私政策。然而,大多数隐私政策都是冗长的,充满了行话,模糊地描述了公司的数据实践和用户的权利。此外,我们的研究表明,超过32%的最终用户发现很难理解解释GDPR要求的隐私政策。因此,对于最终用户和执法机构来说,手动检查公司的隐私政策是否符合GDPR强制执行的要求是一项挑战。在本文中,我们创建了一个包含1,080个网站的隐私政策数据集,由专家根据18项GDPR要求进行注释,并开发了一个基于卷积神经网络(CNN)的模型,该模型可以将隐私政策分类为GDPR要求,准确率为89.2%。我们应用我们的模型自动衡量9761个访问量最大的网站的隐私政策是否符合GDPR。我们的研究结果表明,即使在GDPR生效四年后,68%的网站仍然不符合GDPR的至少一项要求。
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
Is Your Policy Compliant?: A Deep Learning-based Empirical Study of Privacy Policies' Compliance with GDPR
Since the General Data Protection Regulation (GDPR) came into force in May 2018, companies have worked on their data practices to comply with the requirements of GDPR. In particular, since the privacy policy is the essential communication channel for users to understand and control their privacy when using companies' services, many companies updated their privacy policies after GDPR was enforced. However, most privacy policies are verbose, full of jargon, and vaguely describe companies' data practices and users' rights. In addition, our study shows that more than 32% of end users find it difficult to understand the privacy policies explaining GDPR requirements. Therefore, it is challenging for the end users and law enforcement authorities to manually check if companies' privacy policies comply with the requirements enforced by GDPR. In this paper, we create a privacy policy dataset of 1,080 websites annotated by experts with 18 GDPR requirements and develop a Convolutional Neural Network (CNN) based model that can classify the privacy policies into GDPR requirements with an accuracy of 89.2%. We apply our model to automatically measure GDPR compliance in the privacy policies of 9,761 most visited websites. Our results show that, even after four years since GDPR went into effect, 68% of websites still fail to comply with at least one requirement of GDPR.
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
Darwin's Theory of Censorship: Analysing the Evolution of Censored Topics with Dynamic Topic Models PRSONA Tracking the Evolution of Cookie-based Tracking on Facebook Fingerprinting and Personal Information Leakage from Touchscreen Interactions Privacy and Security Evaluation of Mobile Payment Applications Through User-Generated Reviews
×
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