合适的工具：生成式人工智能隐私威胁缓解措施的选择
The Right Tool for the Job: On the Selection of Mitigations for GenAI Privacy Threats
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中文总结 AI 辅助
本文指出GenAI隐私工程的主要挑战是威胁与缓解措施间缺乏桥梁，分解为三个子问题并提出四项建议及研究议程，以推动系统化的缓解措施选择。
中文摘要 AI 辅助
生成式人工智能（GenAI）已迅速从一项实验性技术演变为现代软件系统的基础组成部分。然而，随着其应用日益广泛，保护敏感个人数据变得愈发具有挑战性。具体而言，GenAI系统不仅放大了传统的隐私威胁，还引入了新的基于推理的风险，例如从看似无害的输入中构建详细的用户画像。为此，隐私威胁建模框架开始以更细粒度捕获GenAI特有的隐私威胁。与此同时，越来越多的缓解技术被提出以应对这些威胁。然而，尽管对威胁和缓解措施的认识不断成熟，问题空间与解决方案空间却在很大程度上独立发展。本文立场论文认为，GenAI隐私工程的主要挑战并非缺乏关于隐私威胁或缓解技术的知识，而是二者之间缺失的桥梁。我们将这一差距分解为三个子问题：（i）GenAI系统缺乏细粒度的威胁到缓解措施的映射；（ii）解决方案空间假设在GenAI情境下不适用；（iii）在GenAI约束下优先级排序困难。我们为未来的缓解措施选择方法提出了四项建议，并概述了一种建议方法，将既有的威胁到缓解措施映射方法扩展到GenAI特有的威胁特征。我们提出了一项研究议程，旨在实现基于GenAI的系统更系统化的隐私缓解措施选择。
英文摘要
Generative Artificial Intelligence (GenAI) has rapidly evolved from an experimental technology into a foundational component of modern software systems. However, as its adoption grows, protecting sensitive personal data becomes increasingly challenging. Specifically, GenAI systems not only amplify traditional privacy threats but also introduce new inference-based risks, such as constructing detailed user profiles from seemingly harmless inputs. In response, privacy threat modeling frameworks are beginning to capture GenAI-specific privacy threats with finer granularity. At the same time, a growing number of mitigation techniques have been proposed to address these threats. However, although knowledge of both threats and mitigations continues to mature, the problem- and solution-space have developed largely independently. This position paper argues that the primary challenge in GenAI privacy engineering is not the lack of knowledge about privacy threats or mitigation techniques, but the missing bridge between them. We decompose this gap into three sub-problems: (i) lack of fine-grained threat-to-mitigation mapping for GenAI systems, (ii) inapplicable solution-space assumptions in the GenAI context, and (iii) prioritization difficulty under GenAI constraints. We derive four recommendations for future mitigation-selection approaches, and outline a suggested approach that extends established threat-to-mitigation mapping methods to GenAI-specific threat characteristics. We propose a research agenda toward more systematic privacy mitigation selection for GenAI-based systems.
发表机构
- KU Leuven（荷语鲁汶大学）
机构由 AI 辅助整理，请以论文原文为准。
