Collusion-Resistant Privacy-Preserving Multidimensional Data Aggregation Scheme With Dynamic Membership for the Industrial Internet of Things

Abstract

As industrial devices in the Industrial Internet of Things (IIoT) generate increasing amounts of sensing data, privacy-preserving aggregation has become essential for efficient data collection and analysis in practical industrial environments. However, most existing privacy-preserving data aggregation schemes assume static device participation, lack resistance to collusion attacks between aggregators and data centers (DCs), and incur high computational overhead due to the use of bilinear pairing operations. To overcome these limitations, this article introduces a privacy-preserving multidimensional data aggregation (PPMDA) scheme with dynamic device participation support. In each data encryption process, each sensor node (SN) uses its private key, the public keys of the adjacent SNs, and a time parameter to generate a blinding factor to hide its data, eliminating the need for a trusted third party to preset the blinding factor. Importantly, the scheme supports dynamic device participation, enabling devices to join or leave the system without requiring system reinitialization or disrupting ongoing data aggregation. Security analysis confirms that PPMDA ensures data privacy and authentication while defending against collusive attacks on edge servers (ESs) and DCs. By avoiding pairing operations, PPMDA significantly reduces computational overhead compared to five existing schemes, making it a practical and efficient solution for large-scale and dynamically evolving IIoT environments.