Private Computing Offloading in Edge Cloud via Collaborative Online Learning

Abstract

Computing offloading based on mobile edge computing (MEC) for mobile devices (MDs) has received great attentions in recent years. Strategy selection is an extremely important part of computing offloading, so how to make an optimal decision quickly and accurately during the computing offloading is a difficult point. Furthermore, MDs are likely to leak personal privacy when interacting with edge cloud, and there is also an issue about commercial privacy leakage between different cloud service suppliers. In this paper, we propose the privacy-protected edge cloud computing offloading (EPCO) algorithm based on online learning to improve the efficiency of computing offloading while ensuring the privacy of system users. Simultaneously, EPCO also supports different MDs customize their privacy level. We prove that adding privacy protection mechanism is almost no effect on the convergence of the algorithm. The simulation results validate our conclusion using a real-world dataset.