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Open Access

ARTICLE

Privacy-Preserving Parallel Non-Negative Matrix Factorization with Edge Computing

Wenxuan Yu1, Wenjing Gao1, Jiuru Wang2, Rong Hao1,*, Jia Yu1,*
1 College of Computer Science and Technology, Qingdao University, Qingdao, China
2 School of Information Science and Engineering, Linyi University, Linyi, China
* Corresponding Author: Rong Hao. Email: email; Jia Yu. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.076731

Received 25 November 2025; Accepted 27 January 2026; Published online 18 February 2026

Abstract

Non-negative Matrix Factorization (NMF) is a computationally intensive matrix operation that resource-constrained clients struggle to complete locally. Privacy-preserving outsourcing allows clients to offload heavy computing tasks to powerful servers, effectively solving the problem of local computing difficulties. However, the existing privacy-preserving NMF outsourcing schemes only allow one server to perform outsourcing computation, resulting in low efficiency on the server side. In order to improve the efficiency of outsourcing computation, we propose a privacy-preserving parallel NMF outsourcing scheme with multiple edge servers. We adopt the matrix blocking technique to divide the computation task into multiple subtasks, and design the NMF parallel computation algorithm based on the multiplication updating rule. The proposed scheme implements the parallel outsourcing of non-negative matrix factorization based on multiple edge servers. We use random permutation matrices to encrypt original matrix, thereby protecting data privacy. In addition, we utilize the iterative nature of the NMF algorithm for result verification. Theoretical analysis and experimental results prove the advantages of the proposed scheme.

Keywords

Secure outsourcing computation; non-negative matrix factorization; parallel outsourcing; edge computing

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