An Intelligent Privacy-Preserving Audit Architecture for UAV Swarm Geofence Compliance: A Deconfliction-to-Containment Reduction and Its Security Boundary

Abstract

A privacy-preserving compliance audit architecture for unmanned aerial vehicle (UAV) swarm operations. The central contribution is a deconfliction-to-containment reduction: rather than comparing n trajectories after the fact (a quadratic, disclosure-bound check), a planner assigns pairwise-disjoint spatial tubes before take-off and establishes their separation once, so that each vehicle subsequently attests only that its own samples stayed inside its own tube. Collision-freedom follows as a consequence (Theorem 2), and the pairwise cost is paid a single time at planning. The commitment layer (Layer 1) is implemented and evaluated as a decision-support audit pipeline that produces non-disclosing, tamper-evident audit artifacts via pre-flight Merkle commitments. It is evaluated in an emulated UAV swarm environment with systematic adversarial injection, in configurations up to 200 vehicles × 500 samples (100,000 sample statements), reporting artifact size, commit/prove/verify/disjunction times, and tamper-detection rates. We then formally identify the security boundary of the implemented layer: it provides coordinate hiding and tamper evidence, but cannot by itself make self-reported containment truthful, which we state as a security game and an impossibility result (Theorem 3). We specify the additional soundness layers (range proof, continuity, provenance and freshness, and aggregation) needed for full containment assurance, proving that composing a knowledge-sound range argument closes the gap (Theorem 4). Throughout, we separate the implemented and measured Layer 1 from the specified and proved—but not yet benchmarked—Layers 2–4, and we make no claim of full zero-knowledge geofence compliance, of swarm-scale deployment, or of deployment readiness.