Workflow offloading with privacy preservation in a cloud‐edge environment. (6th April 2022) - Wang, Jin
Abstract: Owing to the limited computing capability of edge cloudlets, use of cloud platforms to provide computation‐intensive services as an assistant should be studied. Offloading, among the key techniques in the cloud‐edge environment, is plagued by the serious problem of privacy disclosure. In addition, the edge does not have access control of terminals, and the controller working the edge that makes offloading decisions, must not be allowed access to any information regarding the contents of the terminal. Thus, this study introduced a system architecture wherein the data was separated from the control plane to minimize systemic risk and optimize the offloading process towards makespan and cost reduction. Traditional methods of privacy preservation are dependent on encryption of total or content awareness. Edge privacy preservation renders maintaining edge delay advantages a challenge because traditional architectures result in edges being engaged with data encryption and decryption. Moreover, in content applications such as video compression and intelligent collaborative applications, users are unaware of content considered private data, and the relative privacy sensitivity and security priority of tasks. The proposed solution avoids side‐channel attacks by eliminating the individual characteristics via risk‐free constraints under a certain amount of entropy at the security level. Further, a smart optimum approach based on MOEA/D was employed to strike the balance between privacy preservation and the cost/latency of activity. This reduces the burden on edge servers, enhances security based on a better offloading strategy, and protects against threats and attacks using a reasonable system architecture. Through experiments on different types of workflow parameters the validity and effectiveness of the offloading algorithm with privacy preservation was demonstrated.