Evaluating Apple Intelligence's Writing Tools for Privacy Against Large Language Model-Based Inference Attacks: Insights from Early Datasets

TL;DR

This paper empirically evaluates how Apple Intelligence's writing tools can modify text to reduce emotion inference attacks by LLMs, highlighting their potential as privacy-preserving mechanisms on user devices.

Contribution

It introduces early datasets and provides the first empirical analysis of Apple Intelligence's text modifications for privacy against LLM-based inference attacks.

Findings

Text modifications can significantly reduce emotion inference by LLMs.

Apple's writing tools show promise as privacy-preserving mechanisms.

Foundations for adaptive rewriting systems to protect user privacy.

Abstract

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.