Privacy-preserving edge computing offloading scheme based on whale optimization algorithm

Abstract

Aiming at the problem of user’s task offloading in mobile edge computing and the potential leakage of location privacy during the offloading process, a privacy-preserving computing offloading scheme based on whale optimization algorithm is proposed. Using differential privacy technology to obfuscate the user's location information, the user can make task offloading decisions according to the obfuscated distance. Considering the delay, energy consumption, and their weighted sum, the offloading problem is modeled as a convex optimization problem. Then, the whale optimization algorithm is adopted to solve this optimization problem to achieve a balance between privacy protection and resource consumption. Experiments are conducted to verify the relationship between the degree of privacy leakage, the computation-offloading cost and real distance, privacy-preserving impact factor, the respective weights of time delay and energy consumption The experimental results show that the offloading scheme proposed in this paper has good performance in terms of cost and privacy protection.

Safe-Learning-Based Location-Privacy-Preserved Task Offloading in Mobile Edge Computing

“…As a distributed computing model that pushes data processing and storage to the network edge, edge computing has been expanding its application scope, but privacy issues have become increasingly prominent. Data encryption, K-anonymity, blockchain, and location perturbation techniques [7,[20][21][22] have been studied in the context of privacy protection in MEC. More specifically, location perturbation is a technique employed to protect privacy by introducing modifications or perturbations to the original location data.…”

“…In [25], Miao et al propose an MEPA privacy-aware framework for MEC that uses differential privacy technology to protect location privacy in the dataset domain. A privacy-preserving computation offloading scheme based on the whale optimization algorithm is proposed in [7]. This scheme uses differential privacy technology to perturb users' locations and makes offloading decisions based on the perturbed distance.…”

“…In order to protect user location privacy while meeting the performance requirements of MEC applications, such as response time and energy consumption, it is necessary to take corresponding technical measures. These measures include location perturbation, anonymization, and differential privacy [7][8][9]. However, current research on location privacy protection in MEC applications has the following major shortcomings: 1.…”

“…Traditional research on balancing privacy protection, energy consumption, and latency has often relied on rule-based approaches, which typically require predefined rules and models [7]. These approaches neglect the dynamic network environment and fail to adapt to complex and changing protection requirements.…”

Unloading optimization of networked vehicles based on improved genetic and particle swarm optimization

“…3)OGA-OPSO calculation 4)GAVECOS [8] calculation : GAVECOS is an offloading decision based on an improved genetic algorithm. Considering the edge and surrounding small mobile base stations.…”