A-GE: Privacy-Preserving Age Assurance via Fine-Grained Motion

Overview & Synopsis

Age assurance faces a fundamental privacy dilemma: meaningful assurance typically requires revealing identity or other sensitive information, whereas privacy-preserving alternatives often provide little assurance. Existing approaches either disclose sensitive information directly or shift the disclosure and trust requirement to an identity provider, while self-declared age provides little meaningful assurance. Thus, a fundamental gap remains between establishing an age property and revealing information beyond what is necessary to establish that property. We present \textit{A-GE} (Age-Grounded Evidence), a privacy-preserving age assurance system that derives age-related evidence from fine-grained motion captured by commodity inertial measurement units (IMUs). A-GE exploits subtle motion dynamics that we hypothesize reflect underlying neuromuscular and physiological processes during ordinary device interaction. It requires neither identity information nor government identification and operates across available sensor sampling rates, including the approximately 60~Hz limit of web-based sensing, with configurable minimum-age thresholds ranging from 14 to 21 years. We evaluate A-GE on more than 600 participants, with separate participant-disjoint cohorts for model development and final evaluation. At a 16-year minimum-age threshold, A-GE achieves up to 93.8\% accuracy with a comparable F1 score across the evaluated configurations. We further demonstrate client-side deployment on Android, iOS, and the web, and examine unintended attribute inference and residual privacy risks, characterizing both the privacy benefits and limitations of deriving age evidence from commodity motion.