信息管理中用于个人隐私保护的语义保留失真方法
Semantics-Preserved Distortion for Personal Privacy Protection in Information Management
AI总结：
针对信息管理中模型易记忆文本敏感信息的问题，本文提出基于邻域分布散度的两种语义保留失真框架，经多任务及医疗场景验证，在隐私保护上比结构改进方法更简单有效。
摘要：
近年来，机器学习尤其是深度学习已对信息管理领域产生重大影响。尽管已有多种策略被提出用于限制模型从原始文本中学习和记忆敏感信息，本文提出了一种更具语言学基础的方法，可在维持语义完整性的前提下对文本进行失真处理。为此，我们利用邻域分布散度（Neighboring Distribution Divergence）——一种用于评估失真过程中语义保留程度的新型指标。基于该指标，我们提出了两种不同的语义保留失真框架：生成式方法和替换式方法。我们在命名实体识别、成分句法分析、机器阅读理解等多种任务上开展评估，证实了该失真技术在个人隐私保护中的可行性与有效性。我们还在NLP领域的三项隐私相关任务中针对属性攻击测试了本文方法，结果表明，相较于结构改进方法，这种基于数据的改进方法更简单有效。此外，我们在特定医疗信息管理场景下探索了隐私保护问题，表明本文方法可有效限制敏感数据记忆，凸显了其实用性。
英文摘要：
In recent years, machine learning - particularly deep learning - has significantly impacted the field of information management. While several strategies have been proposed to restrict models from learning and memorizing sensitive information from raw texts, this paper suggests a more linguistically-grounded approach to distort texts while maintaining semantic integrity. To this end, we leverage Neighboring Distribution Divergence, a novel metric to assess the preservation of semantic meaning during distortion. Building on this metric, we present two distinct frameworks for semantic-preserving distortion: a generative approach and a substitutive approach. Our evaluations across various tasks, including named entity recognition, constituency parsing, and machine reading comprehension, affirm the plausibility and efficacy of our distortion technique in personal privacy protection. We also test our method against attribute attacks in three privacy-focused assignments within the NLP domain, and the findings underscore the simplicity and efficacy of our data-based improvement approach over structural improvement approaches. Moreover, we explore privacy protection in a specific medical information management scenario, showing our method effectively limits sensitive data memorization, underscoring its practicality.
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。
