Privacy protection and service evaluation methods for location-based services in edge computing environments

TL;DR

This paper introduces a novel privacy protection and evaluation framework for location-based services in edge computing, utilizing new models to assess user privacy and service quality effectively.

Contribution

It presents new NPE and POE evaluation models tailored for edge computing location services, integrating learning factors and metadata analysis.

Findings

The proposed models effectively evaluate privacy and service quality.

Experiments confirm the models' accuracy and feasibility.

The framework enhances privacy protection in edge computing environments.

Abstract

This paper proposes a privacy protection and evaluation method for location services based on edge computing environment. By constructing the site service data protection and system evaluation system in the edge computing environment, based on the existing user privacy protection work, the data processing module and service evaluation module are constructed, and the evaluation algorithm is designed. NPE evaluation model and POE evaluation model are designed according to relevant research recommended by IPE. Specifically, in the NPE evaluation model, we regard each user's decision as a group of factors, and propose a method to integrate learning factors. In the poe evaluation model, users' hidden intentions for the next action are understood by unifying metadata information, two time contexts and other different factors. The experiment verifies the effectiveness and feasibility of this method.