期刊
IEEE TRANSACTIONS ON SERVICES COMPUTING
卷 16, 期 5, 页码 3578-3589出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2023.3262839
关键词
Servers; Protocols; Task analysis; Outsourcing; Internet of Things; Edge computing; Systems architecture; Edge computing; matrix determinant; parallel outsourcing; secure outsourcing computation
This paper introduces a parallel outsourcing mechanism based on two edge servers to accelerate the computation of matrix determinant. The computation task is divided into multiple subtasks using the matrix blocking technique, which are then assigned to the edge servers for parallel computation. Additionally, a privacy-preserving matrix transformation technique is proposed to protect data privacy. The correctness, privacy, and verifiability of the protocol are analyzed, and the performance advantage is demonstrated through simulation experiments.
With the widespread deployment of secure outsourcing computation, the resource-constrained client can delegate intensive computation tasks to powerful servers. Matrix determinant computation is a fundamental mathematical operation that has been widely used in IoT applications. This operation is computationally expensive. Nevertheless, the existing secure outsourcing protocols for matrix determinant are all designed based on one cloud server, which cannot well meet the low-latency and real-time computing requirements for the client. To address this issue, we explore accelerating the computation of matrix determinant by parallel outsourcing based on two nearby edge servers and propose the first practical protocol. We use the matrix blocking technique to split the computation task into multiple subtasks, which are parallel outsourced to edge servers for accelerating the computation. Moreover, we propose a privacy-preserving matrix transformation technique for data privacy protection. This technique only involves the operations of matrix-vector multiplication and matrix-matrix addition. It achieves the lightweight computation for the client and supports computational indistinguishability for the blinded input and a uniform distribution. The correctness, privacy and verifiability of the proposed protocol are analyzed. Finally, the performance advantage of the proposed protocol is demonstrated through simulation experiments.
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