PolicyLR：一种隐私策略的逻辑表示
PolicyLR: A Logic Representation For Privacy Policies
AI总结：
提出PolicyLR范式，通过两阶段翻译与蕴含过程将隐私策略转换为机器可读的逻辑表示，实现0.91精确率和0.88召回率，支持合规性检查、不一致性检测和隐私比较购物等下游任务。
摘要：
隐私策略在在线生态系统中至关重要，它定义了服务如何处理用户数据并遵守GDPR和CCPA等法规。然而，其复杂性和频繁的更新往往使得利益相关者难以理解和分析它们。当前利用自然语言处理的自动化分析方法存在局限性。它们通常专注于单个任务，无法捕获策略的完整上下文。我们提出了PolicyLR，这是一种新范式，提供了隐私策略的全面机器可读表示，作为多种下游任务的一体化解决方案。PolicyLR使用原子公式的赋值将隐私策略转换为机器可读格式，允许对合规性和一致性等任务进行形式化定义。我们开发了一个编译器，使用现成的大型语言模型（LLMs）将非结构化策略文本转换为该格式。该编译器将转换任务分解为两阶段的翻译与蕴含过程。该过程考虑隐私策略的完整上下文以推断复杂公式，其中每个公式由更简单的原子公式组成。该模型的优势在于PolicyLR在设计上是可解释的，并基于隐私策略的片段。我们使用ToS;DR（一个社区标注的隐私策略蕴含数据集）评估了该编译器。利用开源LLMs，我们的编译器分别实现了0.91的精确率和0.88的召回率。最后，我们展示了PolicyLR在三个隐私任务中的效用：策略合规性、不一致性检测和隐私比较购物。
英文摘要：
Privacy policies are crucial in the online ecosystem, defining how services handle user data and adhere to regulations such as GDPR and CCPA. However, their complexity and frequent updates often make them difficult for stakeholders to understand and analyze. Current automated analysis methods, which utilize natural language processing, have limitations. They typically focus on individual tasks and fail to capture the full context of the policies. We propose PolicyLR, a new paradigm that offers a comprehensive machine-readable representation of privacy policies, serving as an all-in-one solution for multiple downstream tasks. PolicyLR converts privacy policies into a machine-readable format using valuations of atomic formulae, allowing for formal definitions of tasks like compliance and consistency. We have developed a compiler that transforms unstructured policy text into this format using off-the-shelf Large Language Models (LLMs). This compiler breaks down the transformation task into a two-stage translation and entailment procedure. This procedure considers the full context of the privacy policy to infer a complex formula, where each formula consists of simpler atomic formulae. The advantage of this model is that PolicyLR is interpretable by design and grounded in segments of the privacy policy. We evaluated the compiler using ToS;DR, a community-annotated privacy policy entailment dataset. Utilizing open-source LLMs, our compiler achieves precision and recall values of 0.91 and 0.88, respectively. Finally, we demonstrate the utility of PolicyLR in three privacy tasks: Policy Compliance, Inconsistency Detection, and Privacy Comparison Shopping.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
