  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.
