Pantomime: Motion Data Anonymization using Foundation Motion Models

TL;DR

Pantomime is a novel motion data anonymization method that uses foundation models to generate natural-looking motion sequences, significantly reducing individual identification while preserving data utility.

Contribution

The paper introduces Pantomime, a new approach leveraging foundation motion models for privacy-preserving anonymization of human motion data.

Findings

Reduces identification accuracy to 10%

Maintains naturalness of motion sequences

Preserves utility of anonymized data

Abstract

Human motion is a behavioral biometric trait that can be used to identify individuals and infer private attributes such as medical conditions. This poses a serious threat to privacy as motion extraction from video and motion capture are increasingly used for a variety of applications, including mixed reality, robotics, medicine, and the quantified self. In order to protect the privacy of the tracked individuals, anonymization techniques that preserve the utility of the data are required. However, anonymizing motion data is a challenging task because there are many dependencies in motion sequences (such as physiological constraints) that, if ignored, make the anonymized motion sequence appear unnatural. In this paper, we propose Pantomime, a full-body anonymization technique for motion data, which uses foundation motion models to generate motion sequences that adhere to the dependencies in the data, thus keeping the utility of the anonymized data high. Our results show that Pantomime can maintain the naturalness of the motion sequences while reducing the identification accuracy to 10%.