Preva: Protecting Inference Privacy through Policy-based Video-frame Transformation

Abstract

Real-time edge-cloud video analytics systems have been widely used to support such applications as traffic counting, surveillance, autonomous driving, Metaverse, etc. In such a system, the edge and the cloud cooperatively conduct model inference of the video frames captured by the camera of the edge, using a trained DNN model of the video analytics application. The edge conducts initial analytics on the video frames to a split layer of the DNN model; and then sends intermediate results to the cloud for follow-up analytics. In this paper, we show that an attacker can perform reconstruction attacks to the intermediate results; and private information of the raw video frames, e.g., a plate number of a car, can be leaked. In this paper, we present Preva, a new Privacy preserving Real-time Edge-cloud Video Analytics system. The core idea of Preva is to conduct image transformation on the video frames, as preprocessing, prior to the video frames starting the edge-cloud video analytics process, so that during edge-cloud video analytics, the intermediate results will not leak private information under attack. We design a policy-based video-frame transformation scheme. Given the resource constraints of the edge, Preva ensures high accuracy in the final video analytics results and minimizes privacy leakage in any split layer. We present a formal privacy analysis and we show that Preva can guarantee privacy leakage under the reconstruction attacks of both outsider attackers and insider attackers. We evaluate Preva through three video analytics applications and we show that Preva outperforms existing systems for 64.4% in analytics accuracy and 59.2% in privacy leakage.