Learn Locally, Calibrate Globally: Client-Level Differentially Private Video Anomaly Detection

An anomaly detector can rank moments within a video correctly yet misorder normal and abnormal frames across videos. We exploit this gap to rethink private collaboration: transfer knowledge for cross-video score alignment rather than an entire detection model. In this paper, we propose Public-Anchor Response Decoding for weakly supervised video anomaly detection (PARD-VAD), which learns locally and calibrates globally. Each client projects differences between matched private and public-only teacher responses onto a public function basis. Clipping, secure aggregation, and Gaussian perturbation produce a single two-coefficient differentially private (DP) release while teacher parameters remain local. Public decoding reconstructs temporal evidence, and public-score-weighted pooling converts it into one log-odds correction per video. This changes cross-video rankings while preserving within-video ordering, with inference performed as DP post-processing. For fixed public assets, sensitivity analysis and R\'enyi DP accounting establish client-level replace-one privacy. Using the same release and decoding rule, PARD-VAD achieves 89.19% AUC on UCF-Crime and 84.03% AP on XD-Violence on the complete test sets at a per-deployment budget of $ϵ\le 8$, $\delta =10^{-5}$, exceeding the reported privacy-preserving baselines in our comparison. Matched public-only controls isolate gains of 0.81 and 0.54 percentage points, and an exhaustive frame-pair audit attributes these gains to cross-video ordering.