Personal Data Circulation Mechanisms in Embodied AI: Balancing Privacy Protection and Data Value Release

Yue Ma

doi:10.54691/q1qz0997

Authors

Yue Ma Beijing Institute of Technology, Beijing, China

DOI:

https://doi.org/10.54691/q1qz0997

Keywords:

Embodied AI, data circulation, Privacy-Enhancing Technologies (PETs), enterprise compliance.

Abstract

Embodied AI, characterized by multi-sensor fusion and autonomous decision-making capabilities, has transcended traditional personal information protection rules, giving rise to multiple privacy risks including physical privacy and mental privacy concerns, and causing legal principles such as "informed consent," purpose limitation, and data minimization to face application difficulties. However, current academic research still primarily focuses on personal information protection as the main approach to regulating embodied AI, without systematically addressing the circulation of personal information-related embodied AI data. With the goal of balancing personal information protection and data value release, a regulatory philosophy distinguishing personal information from personal data should be established. Personal information should be transformed into circulatable data through gradient anonymization processing using technologies such as Differential Privacy (DP) and Federated Learning (FL), shifting from the user "informed consent" model to enterprise proactive prevention obligations for personal information leakage. Institutional safeguards should be constructed from dimensions including privacy tort liability delineation, introduction of Data Intermediaries, and regulatory mechanism innovation, to resolve compliance dilemmas, mitigate data leakage and re-identification risks, and provide theoretical support and practical guidance for compliant and efficient circulation of embodied AI datasets.

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