Verifiable data mining against malicious adversaries in industrial internet of things

• Main Authors: MA, Zhuoran, MA, Jianfeng, MIAO, Yinbin, LIU, Ximeng, CHOO, Kim-Kwang Raymond, GAO, Yu, DENG, Robert H.
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: 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; Information Security
• Online Access: https://ink.library.smu.edu.sg/sis_research/7243
• Institution: Singapore Management University

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.