梳理AI时代19类隐私风险，揭示人为失误是最大威胁
Privacy in the Age of AI: A Taxonomy of Data Risks
- 从45篇研究中提炼出四大类19项AI隐私风险
- 人为失误占比9.45%，高于技术漏洞等传统风险
- 兼顾技术和行为因素，适合安全与伦理研究者
人工智能系统处理日益敏感的数据，带来前所未有的隐私挑战。传统隐私框架因自主学习、黑箱决策等特性而难以适用。本文通过系统综述45项研究，构建了一个涵盖数据集级、模型级、基础设施级和内部威胁四类的隐私风险分类体系，识别出19项关键风险。研究发现各类风险分布均衡，其中人为失误占比达9.45%，成为最显著因素。该分类体系突破了传统安全策略过度依赖技术控制的局限，强调对技术与行为维度的整合理解，为可信赖AI发展提供理论基础，并推动未来研究深化。
原文摘要 · Abstract (English)
Artificial Intelligence (AI) systems introduce unprecedented privacy challenges as they process increasingly sensitive data. Traditional privacy frameworks prove inadequate for AI technologies due to unique characteristics such as autonomous learning and black-box decision-making. This paper presents a taxonomy classifying AI privacy risks, synthesised from 45 studies identified through systematic review. We identify 19 key risks grouped under four categories: Dataset-Level, Model-Level, Infrastructure-Level, and Insider Threat Risks. Findings reveal a balanced distribution across these dimensions, with human error (9.45%) emerging as the most significant factor. This taxonomy challenges conventional security approaches that typically prioritise technical controls over human factors, highlighting gaps in holistic understanding. By bridging technical and behavioural dimensions of AI privacy, this paper contributes to advancing trustworthy AI development and provides a foundation for future research.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。