理解与缓解 Vec2Text 对稠密检索系统的威胁
Understanding and Mitigating the Threat of Vec2Text to Dense Retrieval Systems
AI总结：
本文研究了影响 Vec2Text 文本嵌入反演攻击稠密检索系统时文本可恢复性的多种因素，并提出了一种在保证排序有效性同时降低恢复风险的嵌入变换修复方法。
摘要：
Vec2Text——一种文本嵌入反演方法——的出现，引发了人们对使用文本嵌入的稠密检索系统（如 OpenAI 和 Cohere 提供的系统）的严重隐私担忧。这种威胁源于恶意攻击者在获取嵌入后能够重构原始文本的能力。在本文中，我们研究了与嵌入模型相关的各种可能影响 Vec2Text 文本可恢复性的因素。我们探索了距离度量、池化函数、瓶颈预训练、加入噪声训练、嵌入量化和嵌入维度等因素，这些因素在原始 Vec2Text 论文中未被考虑。通过对这些因素进行全面分析，我们的目标是更深入地理解影响稠密检索系统中文本可恢复性与检索有效性之间权衡的关键要素，为设计隐私感知的稠密检索系统的从业者提供见解。我们还提出了一种简单的嵌入变换修复方法，在保证同等排序有效性的同时降低可恢复性风险。总体而言，本研究表明 Vec2Text 可能对当前稠密检索系统构成威胁，但存在一些有效方法来修补此类系统。
英文摘要：
The emergence of Vec2Text -- a method for text embedding inversion -- has raised serious privacy concerns for dense retrieval systems which use text embeddings, such as those offered by OpenAI and Cohere. This threat comes from the ability for a malicious attacker with access to embeddings to reconstruct the original text. In this paper, we investigate various factors related to embedding models that may impact text recoverability via Vec2Text. We explore factors such as distance metrics, pooling functions, bottleneck pre-training, training with noise addition, embedding quantization, and embedding dimensions, which were not considered in the original Vec2Text paper. Through a comprehensive analysis of these factors, our objective is to gain a deeper understanding of the key elements that affect the trade-offs between the text recoverability and retrieval effectiveness of dense retrieval systems, offering insights for practitioners designing privacy-aware dense retrieval systems. We also propose a simple embedding transformation fix that guarantees equal ranking effectiveness while mitigating the recoverability risk. Overall, this study reveals that Vec2Text could pose a threat to current dense retrieval systems, but there are some effective methods to patch such systems.
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。
