Location-based augmented reality (LB-AR) applications, such as Pokemon Go, rely on sub-second GPS updates to deliver responsive and immersive user experiences. However, this high-frequency location reporting introduces serious privacy risks. Unlike traditional Location-Based Services (LBS), LB-AR demands real-time protection under strict latency and quality-of-service (QoS) constraints, while providing strong per-location and trajectory-level privacy guarantees. Existing privacy mechanisms struggle to satisfy these requirements: they either introduce prohibitive latency, significantly degrade application utility, or fail to defend against trajectory inference attacks. To address this challenge, we present PrivAR, the first client-side privacy framework for real-time LB-AR. PrivAR introduces two lightweight mechanisms: (i) Planar Staircase Mechanism (PSM), which uses a staircase-shaped distribution to generate noisy locations with strong per-location privacy, low expected distortion, and minimal computational overhead; and (ii) Planar Staircase Mechanism with Intermediate (PSM-I), an extension of PSM that generates a device-resident intermediate trajectory and selectively reuses previously perturbed outputs when insufficient drift is observed, thereby strengthening trace-level privacy while preserving high QoS. We provide theoretical analysis, extensive evaluation on two public mobility datasets and our GeoTrace dataset, and validate PrivAR in a Pokemon GO-style Android prototype. Results show that PrivAR improves AR QoS (game score) by up to 50% and increases attacker Bayes risk by up to 1.8x, while incurring only 0.06 ms of per-update overhead (less than 0.2% of end-to-end latency).

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