Personalized and Differential Privacy-Aware Video Stream Offloading in Mobile Edge Computing

Abstract

In Mobile Edge Computing (MEC), the collaboration between end devices and servers guarantees the low-latency and high-accuracy video stream analysis. However, such paradigm of video stream offloading poses a serious threat to the location privacy and the usage pattern privacy of end devices. The existing works offer strict privacy guarantee for users, but they do not take the features of video stream into consideration, thus leading to the relatively higher computation cost. To tackle this issue, we propose a personalized and differential privacy-aware video stream offloading scheme that supports users personalized and time-varying privacy requirements, provides corresponding differential privacy preservation, and generates minimal latency and energy cost. Specifically, we formulate an NP-hard optimization that jointly optimizes the video frame rate, frame resolution and offloading ratio to maximize the analysis accuracy of video stream and minimize the energy cost and the latency subject to the channel bandwidth, computing resources, and personalized and time-varying privacy requirements. Then, we design a online learning-based and personalized privacy-aware video stream offloading algorithm for the optimization problem and thereby obtain the optimal video stream offloading scheme. Last, the extensive experimental results validate the superior performance of the proposed scheme, compared to the three latest existing works.