A Privacy-preserving Edge-cloud Video Analytics System via Policy-based Frame Transformation

In real-time edge-cloud video analytics systems, the edge conducts initial analytics on the video frames to a split layer of a trained neural network model. Then, it sends intermediate results to the cloud for follow-up analytics. In this article, we first show that malicious attackers can perform reconstruction attacks and attribute inference attacks on those intermediate results. We present Preva, a new <ani:underline>P</ani:underline>rivacy-preserving <ani:underline>R</ani:underline>eal-time <ani:underline>E</ani:underline>dge-cloud <ani:underline>V</ani:underline>ideo <ani:underline>A</ani:underline>nalytics system that defends against both attacks while respecting latency constraints. Preva first applies a lightweight, policy-based video frames transformation scheme generated on the fly by PrevaNetv2, with a lightweight backbone and an early-exit mechanism that adapts the computation resources of users. To guarantee end-to-end latency, a contextual multi-armed-bandit algorithm (BBSplit) dynamically chooses the optimal split layer, and a frame-similarity filter (PPReuse) amortizes policy generation across video frames. We present a formal privacy analysis and show that Preva can guarantee privacy leakage under reconstruction attacks and attribute inference attacks. We evaluate Preva through three video analytics applications and show that Preva outperforms existing systems by 41.6% in analytics accuracy and 52.7% in privacy leakage.