Efficient Privacy-Preserving Multi-Functional Data Aggregation Scheme for Multi-Tier IoT System

The proliferation of Internet of Things (IoT) devices has led to the generation of massive amounts of data that require efficient aggregation for analysis and decision-making. However, multi-tier IoT systems, which involve multiple layers of devices and gateways, face more complex security challenges in data aggregation compared to ordinary IoT systems. In this paper, we propose an efficient privacy-preserving multi-functional data aggregation scheme for multi-tier IoT architecture. The scheme supports privacy-preserving calculation of mean, variance, and anomaly proportion. The scheme uses the Paillier cryptosystem and the BLS algorithm for encryption and signature, and uses blinding techniques to keep the size of the IoT system secret. In order to make the Paillier algorithm more suitable for the IoT scenario, we also improve its efficiency of encryption and decryption. The performance evaluation shows that the scheme improves encryption efficiency by 43.7% and decryption efficiency by 45% compared to the existing scheme.

I. Introduction

Widespread deployment of the Internet of Things (IoT) has revolutionized our production mode and life style by providing various IoT applications, such as smart transportation, smart buildings and smart grid [1]–[3]. Usually, in an IoT system, terminal sensor devices are responsible for collecting and uploading data, while cloud servers are responsible for processing data and issuing instructions. However, due to the multi-tier and complex structure often found in IoT systems, as well as the communication bandwidth limitations of network infrastructure, cloud computing alone is unable to support this ubiquitous deployment and application of IoT programs. [4] Multi-tier IoT systems require support from multi-tier computing structures, where cloud computing, fog computing, and edge computing are respectively developed for regional, local, and device-level applications, respectively. Data aggregation in multi-tier IoT systems is a critical process that involves collecting data from multiple tiers of the system hierarchy, summarizing the data, and presenting meaningful insights for decision-making and optimization. Compared with traditional IoT systems, multi-tier IoT systems face more privacy challenges in data aggregation due to the introduction of cloud computing, fog computing, and edge computing. The privacy protection challenges faced by multi-tiered IoT systems during data aggregation include the potential for unauthorized access, data breaches, and data misuse due to the sensitive nature of the data generated by multiple tiers of the system hierarchy. [5] How to protect privacy during data aggregation across the multi-tier structure of the IoT has become a crucial issue.