用NLP对比欧美隐私法差异，助力跨国企业合规
Machine Learning-Driven Convergence Analysis in Multijurisdictional Compliance Using BERT and K-Means Clustering
- 用BERT与聚类分析法律文本，识别GDPR与CCPA异同
- 发现‘被遗忘权’与‘退出销售权’在执行层面存在关键差异
- 为跨境公司提供可落地的合规策略，适合法律科技从业者
随着数字数据持续增长，亟需有效的监管机制来保护个人信息。加州《消费者隐私法案》（CCPA）与欧盟《通用数据保护条例》（GDPR）是两大重要隐私法规，但其范围、定义和执法方式差异显著。本文提出一种基于机器学习的自适应合规方法，以自然语言处理（NLP）为核心，对比分析GDPR与CCPA的法律文本，聚焦如‘被遗忘权’与‘选择退出销售’等具体条款。研究通过BERT模型与K-Means聚类技术，识别两法规在适用场景、责任主体及执行要求上的重叠与分歧。结果表明，尽管目标相似，但在数据删除请求响应流程、用户权利行使门槛等方面存在结构性差异。该研究旨在弥合法律知识与技术能力之间的鸿沟，为跨国企业提供更高效、更精准的合规策略，并探讨了在法律文献中应用NLP的挑战与优化路径。
原文摘要 · Abstract (English)
Digital data continues to grow, there has been a shift towards using effective regulatory mechanisms to safeguard personal information. The CCPA of California and the General Data Protection Regulation (GDPR) of the European Union are two of the most important privacy laws. The regulation is intended to safeguard consumer privacy, but it varies greatly in scope, definitions, and methods of enforcement. This paper presents a fresh approach to adaptive compliance, using machine learning and emphasizing natural language processing (NLP) as the primary focus of comparison between the GDPR and CCPA. Using NLP, this study compares various regulations to identify areas where they overlap or diverge. This includes the "right to be forgotten" provision in the GDPR and the "opt-out of sale" provision under CCPA. International companies can learn valuable lessons from this report, as it outlines strategies for better enforcement of laws across different nations. Additionally, the paper discusses the challenges of utilizing NLP in legal literature and proposes methods to enhance the model-ability of machine learning models for studying regulations. The study's objective is to "bridge the gap between legal knowledge and technical expertise" by developing regulatory compliance strategies that are more efficient in operation and more effective in data protection.
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