# Exploring Audio Editing Features as User-Centric Privacy Defenses Against Large Language Model(LLM) Based Emotion Inference Attacks

arXiv: https://arxiv.org/abs/2501.18727 · HTML: https://mlsanity.com/paper/2501.18727/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 40 references that are in the map.
- First posted on arXiv 30 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [SECap: Speech Emotion Captioning with Large Language Model](https://mlsanity.com/paper/2312.10381/) (2023)
- [Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances](https://mlsanity.com/paper/2407.21315/) (2024)
- [Privacy-preserving Voice Analysis via Disentangled Representations](https://mlsanity.com/paper/2007.15064/) (2020)
- [Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings](https://mlsanity.com/paper/2112.13416/) (2021)
- [User-Level Differential Privacy against Attribute Inference Attack of Speech Emotion Recognition in Federated Learning](https://mlsanity.com/paper/2204.02500/) (2022)
- [SoK: Data Privacy in Virtual Reality](https://mlsanity.com/paper/2301.05940/) (2023)
- [Zooming Into Video Conferencing Privacy and Security Threats](https://mlsanity.com/paper/2007.01059/) (2020)
- [New Challenges for Content Privacy in Speech and Audio](https://mlsanity.com/paper/2301.08925/) (2023)
- [Privacy against Real-Time Speech Emotion Detection via Acoustic Adversarial Evasion of Machine Learning](https://mlsanity.com/paper/2211.09273/) (2022)
- [Emotion Recognition in Audio and Video Using Deep Neural Networks](https://mlsanity.com/paper/2006.08129/) (2020)
- [Music Enhances Activity in the Hypothalamus, Brainstem, and Anterior Cerebellum during Script-Driven Imagery of Affective Scenes](https://mlsanity.com/paper/2301.10914/) (2023)

## Abstract (arXiv)

The rapid proliferation of speech-enabled technologies, including virtual assistants, video conferencing platforms, and wearable devices, has raised significant privacy concerns, particularly regarding the inference of sensitive emotional information from audio data. Existing privacy-preserving methods often compromise usability and security, limiting their adoption in practical scenarios. This paper introduces a novel, user-centric approach that leverages familiar audio editing techniques, specifically pitch and tempo manipulation, to protect emotional privacy without sacrificing usability. By analyzing popular audio editing applications on Android and iOS platforms, we identified these features as both widely available and usable. We rigorously evaluated their effectiveness against a threat model, considering adversarial attacks from diverse sources, including Deep Neural Networks (DNNs), Large Language Models (LLMs), and and reversibility testing. Our experiments, conducted on three distinct datasets, demonstrate that pitch and tempo manipulation effectively obfuscates emotional data. Additionally, we explore the design principles for lightweight, on-device implementation to ensure broad applicability across various devices and platforms.