用AI帮用户自动分析隐私政策，只提醒真正违规的4.8%内容。
Let's Measure the Elephant in the Room: Facilitating Personalized Automated Analysis of Privacy Policies at Scale
- 结合NLP与逻辑推理，把政策和用户偏好转为可比形式。
- 在100大网站上分析，95.2%内容无冲突，仅4.8%需关注。
- 适合关心数据隐私但没时间读条款的普通用户。
现代人拥有众多在线账户，却很少真正阅读服务条款或隐私政策。本文提出PoliAnalyzer，一个神经符号系统，帮助用户个性化分析隐私政策。该系统利用自然语言处理提取政策文本中的数据使用行为，并通过确定性逻辑推理，将用户偏好与政策表示进行比对，生成合规报告。为此，我们扩展了现有正式的数据使用条款语言，将隐私政策建模为应用策略，用户偏好建模为数据策略。在法律专家构建的增强版PolicyIE数据集上，PoliAnalyzer在识别相关数据使用行为任务中取得90%-100%的F1分数。进一步，基于23种用户画像，分析前100大访问网站的隐私政策，发现平均95.2%的政策段落与用户偏好无冲突，仅4.8%（636 / 13205）存在违反，显著降低认知负担。同时识别出常见违规实践，如位置数据被共享给第三方。结果表明，PoliAnalyzer能借助现成NLP工具实现大规模个性化隐私政策自动化分析，为个体夺回数据控制权提供路径，并推动社会讨论平台数据实践，促进更公平的权力关系。
原文摘要 · Abstract (English)
In modern times, people have numerous online accounts, but they rarely read the Terms of Service or Privacy Policy of those sites despite claiming otherwise. This paper introduces PoliAnalyzer, a neuro-symbolic system that assists users with personalized privacy policy analysis. PoliAnalyzer uses Natural Language Processing (NLP) to extract formal representations of data usage practices from policy texts. In favor of deterministic, logical inference is applied to compare user preferences with the formal privacy policy representation and produce a compliance report. To achieve this, we extend an existing formal Data Terms of Use policy language to model privacy policies as app policies and user preferences as data policies. In our evaluation using our enriched PolicyIE dataset curated by legal experts, PoliAnalyzer demonstrated high accuracy in identifying relevant data usage practices, achieving F1-score of 90-100% across most tasks. Additionally, we demonstrate how PoliAnalyzer can model diverse user data-sharing preferences, derived from prior research as 23 user profiles, and perform compliance analysis against the top 100 most-visited websites. This analysis revealed that, on average, 95.2% of a privacy policy's segments do not conflict with the analyzed user preferences, enabling users to concentrate on understanding the 4.8% (636 / 13205) that violates preferences, significantly reducing cognitive burden. Further, we identified common practices in privacy policies that violate user expectations - such as the sharing of location data with 3rd parties. This paper demonstrates that PoliAnalyzer can support automated personalized privacy policy analysis at scale using off-the-shelf NLP tools. This sheds light on a pathway to help individuals regain control over their data and encourage societal discussions on platform data practices to promote a fairer power dynamic.
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