Cost-effective provable secure cloud storage self-auditing scheme for big data in WMSNS
by Xiaojun Zhang; Jie Zhao; Liming Mu; Xinpeng Zhang
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 11, No. 4, 2019

Abstract: Medical big data have recently received considerable attention in the modern medical systems, since they give great opportunities to mine new medical knowledge. In the wireless medical sensor networks (WMSNs), medical big data can be generated and processed everywhere at any time. With the rapid development of cloud computing, cloud-based WMSNs can provide more efficient processing of patients' physiology parameters and support richer storage services. Meanwhile, the integrity of medical big data becomes significant, since medical big data will be employed to provide the medical diagnosis and other medical treatments. In this paper, we propose a cost-effective self-auditing scheme for cloud storage medical big data without pairings. In the proposed scheme, a patient can personally check the medical big data integrity effectively, without retrieving the entire medical big data, and thus dramatically reduces the communication overhead. Moreover, we extend the proposed scheme to a batch self-auditing scheme, such that a patient can efficiently perform self-auditing for multiple different medical big data files simultaneously. The performance comparison shows that the proposed scheme is much more light-weight, and more practical in WMSNs.

Online publication date: Mon, 30-Sep-2019

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