测试苹果写作工具如何通过改写文本保护用户情绪隐私
Evaluating Apple Intelligence's Writing Tools for Privacy Against Large Language Model-Based Inference Attacks: Insights from Early Datasets
- 用新构建的数据集评估苹果工具的文本改写效果
- 改写和语气调整可显著降低大模型的情绪识别准确率
- 适合关注设备端隐私保护的研究者与开发者
大型语言模型（LLMs）利用文本推断情绪的攻击行为严重威胁用户隐私。本文针对苹果智能写作工具在iPhone、iPad和MacBook上的集成应用，研究其通过重写与语气调整等文本修改手段缓解此类风险的潜力。我们构建了首个专门用于该目的的早期数据集，实证评估不同文本修改对基于LLM的情绪检测影响。结果表明，苹果写作工具具备作为隐私保护机制的强潜力。研究为未来动态中性化敏感情感内容的自适应重写系统奠定基础，旨在实现设备端、以用户为中心的隐私保护机制，防范部署系统中针对大模型的高级推理攻击。据我们所知，这是首次在隐私保护背景下对苹果智能写作工具进行的实证分析。
原文摘要 · Abstract (English)
The misuse of Large Language Models (LLMs) to infer emotions from text for malicious purposes, known as emotion inference attacks, poses a significant threat to user privacy. In this paper, we investigate the potential of Apple Intelligence's writing tools, integrated across iPhone, iPad, and MacBook, to mitigate these risks through text modifications such as rewriting and tone adjustment. By developing early novel datasets specifically for this purpose, we empirically assess how different text modifications influence LLM-based detection. This capability suggests strong potential for Apple Intelligence's writing tools as privacy-preserving mechanisms. Our findings lay the groundwork for future adaptive rewriting systems capable of dynamically neutralizing sensitive emotional content to enhance user privacy. To the best of our knowledge, this research provides the first empirical analysis of Apple Intelligence's text-modification tools within a privacy-preservation context with the broader goal of developing on-device, user-centric privacy-preserving mechanisms to protect against LLMs-based advanced inference attacks on deployed systems.
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