Consent-Aware Generative Privacy Architecture for Wearable Imaging Systems

This defensive technical publication describes a privacy architecture for smart glasses, wearable cameras, spatial-computing systems, and other imaging devices in which recognizable human identity is treated as permissioned information within the capture pipeline. The system detects people, resolves a minimum-disclosure capture-authorization state where available, applies contextual policy, and preserves an identifiable likeness only when the relevant authorization permits it. Otherwise, the system may generate a synthetic non-identifying surrogate, conventionally anonymize or remove the subject, or suppress durable storage. The publication focuses on combination-level implementations including portable capture-authorization credentials usable across manufacturers and identity ecosystems; subject-pushed privacy matching that does not require identifying nonparticipants; per-subject and per-modality authorization; trusted pre-storage generative transformation with optional source zeroization; confidence-based fail-closed behavior; distribution-time re-evaluation; identity-free privacy provenance; guardian authorization; downstream region-level enforcement; and session-scoped synthetic identity binding. It expressly distinguishes these combinations from prior work on smart-glasses bystander privacy, consent-based restoration, generative face anonymization, privacy signaling, trusted camera mediation, and federated identity. The publication is intentionally released as a defensive disclosure. Its text and figures are licensed CC BY 4.0, with specified example schemas/protocol descriptions additionally released under CC0, and it includes a personal patent non-assertion limited to rights held by the author.