A Lightweight and Privacy-preserving Distributed Multidimensional Data Trend Query Scheme with Fault-tolerant for Machine-as-a-Service

Zhao, Tianci and Zhou, Yuanjian and Zhang, Chi and Meng, Weizhi and Jing, Zhengjun (2025) A Lightweight and Privacy-preserving Distributed Multidimensional Data Trend Query Scheme with Fault-tolerant for Machine-as-a-Service. IEEE Internet of Things Journal. ISSN 2327-4662

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Abstract

In the Machine-as-a-Service (MaaS) model, enterprises can significantly reduce production costs by leasing devices from original equipment manufacturers (OEM), while OEM can enhance device quality by utilizing device data shared by enterprises. As such, MaaS is emerging as a very promising paradigm in modern manufacturing. However, the multidimensional data trend formed by the multidimensional data may leak the private production data of enterprises, particularly when the OEM leases the same type of device to enterprises. Currently, there is no targeted and feasible solution to ensure the privacy, integrity, and fault-tolerant of multi-user and multidimensional data in the MaaS model. To address this challenge, we propose a lightweight, privacy-preserving and fault-tolerant distributed multidimensional data trend query scheme for MaaS. The proposed scheme ensures multidimensional data privacy through local differential privacy (LDP), and guarantees fault-tolerant and data integrity using Shamir secret sharing and hash-based message authentication code (mac). To protect the privacy of multidimensional data aggregation trend, we design a weighted noise injection query algorithm based on LDP. Additionally, the scheme mitigates the risk of data leakage by introducing the blockchain (BC) instead of cloud server (CS). We formally prove the security of our proposed scheme, and the experimental evaluation demonstrates that it outperforms existing schemes in terms of computation and communication overhead.