Verifiable data mining against malicious adversaries in industrial internet of things

Publication Type

Journal Article

Publication Date

2-2022

Abstract

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

Keywords

Industrial Internet of Things, Servers, Cloud computing, Cryptography, Training, Computational modeling, Informatics, Industrial Internet of Things (IIoT), linear regression (LR), malicious adversaries, privacy-preserving, verifiable

Discipline

Information Security

Research Areas

Data Science and Engineering; Cybersecurity

Publication

IEEE Transactions on Industrial Informatics

Volume

18

Issue

2

First Page

953

Last Page

964

ISSN

1551-3203

Identifier

10.1109/TII.2021.3077005

Publisher

Institute of Electrical and Electronics Engineers

Additional URL

https://doi.org/10.1109/TII.2021.3077005

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