MaskAnyone Toolkit: Offering Strategies for Minimizing Privacy Risks and Maximizing Utility in Audio-Visual Data Archiving

TL;DR

MaskAnyone is a new toolkit that helps researchers anonymize audio-visual data by face-swapping and voice alteration, balancing privacy concerns with data utility for social science research.

Contribution

The paper presents MaskAnyone, a scalable, user-friendly toolkit for de-identifying individuals in audio-visual data, integrating privacy-preserving techniques into research workflows.

Findings

Supports multi-person masking and real-time processing

Enhances data privacy while maintaining research utility

Provides a framework for ethical evaluation of masking tools

Abstract

This paper introduces MaskAnyone, a novel toolkit designed to navigate some privacy and ethical concerns of sharing audio-visual data in research. MaskAnyone offers a scalable, user-friendly solution for de-identifying individuals in video and audio content through face-swapping and voice alteration, supporting multi-person masking and real-time bulk processing. By integrating this tool within research practices, we aim to enhance data reproducibility and utility in social science research. Our approach draws on Design Science Research, proposing that MaskAnyone can facilitate safer data sharing and potentially reduce the storage of fully identifiable data. We discuss the development and capabilities of MaskAnyone, explore its integration into ethical research practices, and consider the broader implications of audio-visual data masking, including issues of consent and the risk of misuse. The paper concludes with a preliminary evaluation framework for assessing the effectiveness and ethical integration of masking tools in such research settings.