研究在线图文模型中图像隐私泄露风险及保护方法
Assessing Privacy Preservation and Utility in Online Vision-Language Models
- 分析图像上下文关系如何暴露个人身份信息
- 提出兼顾隐私保护与图像可用性的技术方案
- 适合关注AI隐私安全的研究者与开发者
在线视觉语言模型（OVLMs）在处理图像时带来显著隐私风险，用户上传图像以获取各类服务，却往往未意识到潜在的隐私泄露问题。图像中蕴含的上下文关系可能关联到个人身份信息（PII），即使看似无害的细节，也可能通过周围线索间接揭示敏感信息。本文探讨图像上传至OVLMs时的PII泄露问题及其对用户隐私的影响。研究发现，从图像中提取上下文关系可能导致直接（显式）或间接（隐式）的PII暴露，严重威胁个人隐私。为此，本文提出在保持图像在视觉语言模型应用中预期用途的前提下，保护隐私的有效方法。评估结果表明，该技术能有效平衡隐私保护与功能实用性，凸显了在线图像处理环境中隐私与效用之间的微妙平衡。
原文摘要 · Abstract (English)
The increasing use of Online Vision Language Models (OVLMs) for processing images has introduced significant privacy risks, as individuals frequently upload images for various utilities, unaware of the potential for privacy violations. Images contain relationships that relate to Personally Identifiable Information (PII), where even seemingly harmless details can indirectly reveal sensitive information through surrounding clues. This paper explores the critical issue of PII disclosure in images uploaded to OVLMs and its implications for user privacy. We investigate how the extraction of contextual relationships from images can lead to direct (explicit) or indirect (implicit) exposure of PII, significantly compromising personal privacy. Furthermore, we propose methods to protect privacy while preserving the intended utility of the images in Vision Language Model (VLM)-based applications. Our evaluation demonstrates the efficacy of these techniques, highlighting the delicate balance between maintaining utility and protecting privacy in online image processing environments. Index Terms-Personally Identifiable Information (PII), Privacy, Utility, privacy concerns, sensitive information
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