Big Data Privacy Preserving in Multi-Access Edge Computing for Heterogeneous Internet of Things

TLDR

With the popularity of smart devices, multi‑access edge computing (MEC) has become mainstream for big data in heterogeneous IoT, leveraging edge node computing to reduce data center load and ease storage and processing, but its decentralized nature makes it vulnerable to hacker attacks that can compromise nodes and trigger privacy issues. The paper aims to review MEC architecture for H‑IoT, highlight privacy concerns in data aggregation, mining, and machine‑learning applications, and outline open research questions. We analyze MEC architecture, identify privacy vulnerabilities in data aggregation and mining, and use a machine‑learning privacy‑preserving case study to illustrate the approach. Simulations demonstrate the feasibility of the proposed privacy‑preserving approach.

Abstract

With the popularity of smart devices, multi-access edge computing (MEC) has become the mainstream of dealing with big data in heterogeneous Internet of Things (H-IoT). MEC makes full use of the computing power of edge nodes, which greatly reduces the computing pressure of data centers, and brings great convenience to the storage and processing of big data. However, it is easy to become the object of hacker attacks due to the lack of centralized management of distributed nodes. Once these nodes are compromised, a series of privacy issues can happen. In this article, we first overview the architecture of MEC for H-IoT. The MEC covers three-level advanced functional entities, including moblie edge (ME) system-level, ME host-level and ME network- level. Second, we draw our attention to the privacy issues in the MEC, especially in data aggregation and data mining. In addition, we consider machine learning privacy preserving as a case study in the application of MEC. Simulation results are shown to reveal the feasibility of the proposed method. Finally, we propose open issues for future work.