Article: A mathematical model of attribute-based encryption for mining clusters in big data Journal: International Journal of Business Intelligence and Data Mining (IJBIDM) 2026 Vol.28 No.10 pp.49 - 67 Abstract: Traditional encryption methods make it difficult to meet the fine-grained requirements of multi-user collaboration and dynamic access in big data scenarios, and there are still shortcomings in terms of recall rate, attribute hiding success rate, and encryption efficiency. Therefore, this article proposes a mathematical modelling method for encrypting big data attributes based on clustering mining. The structured dynamic fusion deep graph clustering framework enables effective big data cluster analysis with comprehensive result processing and completion. A bilinear group-based cryptographic model incorporating attribute policies is constructed, encompassing fundamental procedures including system setup, key derivation, index encryption, and trapdoor computation, thereby establishing mathematical representation for attribute-based big data encryption. Experimental evaluations demonstrate the approach achieves 97.85% average recall, 98.7% successful attribute concealment rate, with merely 75.44 ms average encryption latency. Inderscience Publishers - linking academia, business and industry through research

Title: A mathematical model of attribute-based encryption for mining clusters in big data

Authors: Yumei Zhao; Huaxi Chen

Addresses: School of Mathematics and Physics, Bengbu University, Bengbu, 233030, China ' School of Mathematics and Physics, Bengbu University, Bengbu, 233030, China

Abstract: Traditional encryption methods make it difficult to meet the fine-grained requirements of multi-user collaboration and dynamic access in big data scenarios, and there are still shortcomings in terms of recall rate, attribute hiding success rate, and encryption efficiency. Therefore, this article proposes a mathematical modelling method for encrypting big data attributes based on clustering mining. The structured dynamic fusion deep graph clustering framework enables effective big data cluster analysis with comprehensive result processing and completion. A bilinear group-based cryptographic model incorporating attribute policies is constructed, encompassing fundamental procedures including system setup, key derivation, index encryption, and trapdoor computation, thereby establishing mathematical representation for attribute-based big data encryption. Experimental evaluations demonstrate the approach achieves 97.85% average recall, 98.7% successful attribute concealment rate, with merely 75.44 ms average encryption latency.

Keywords: cluster mining; big data attribute encryption; mathematical modelling; deep map clustering mining network; bilinear group.

DOI: 10.1504/IJBIDM.2026.156179

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.10, pp.49 - 67

Received: 19 Dec 2025
Accepted: 13 Mar 2026
Published online: 07 Sep 2026 *

Title: A mathematical model of attribute-based encryption for mining clusters in big data

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