Aya: “An efficient access-controlled storage and processing for cloud-based sensed data”

Konan, Martin; Wang, Wenyong

doi:10.1109/iccwamtip.2015.7493961

Cited by 5 publications

(7 citation statements)

References 8 publications

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“…Similarly, the AYA model of K. Martin et al [44] solves data accountability issues by introducing a new concept of trusted logical agent as a private cloud with data owner control. The data owner is responsible for the processing and storage of his/her data on an outsourced private cloud.…”

Section: E-healthcarementioning

confidence: 99%

“…Denial of Service (Dos) Attack [42,54] Prevention of Ciphertext-only attack [42] Patient Privacy [50] [42] [46] Patient Control [43,44,53] Source Authentication [37] [ 43,44] Dynamic Data operation [43] Audit Control [43,44] Attribute revocation [39] [43]…”

Section: Electroencephalography Electroencephalography Ormentioning

confidence: 99%

“…Most studies have adopted simulations of the techniques to evaluate the performance of the patient data privacy and security in S-CI. Simulation encryption with operation analysis [2], simulation with NS-2 simulator [3,52,54], and implantation with Jpair library and Netbeans for algorithm [44,53] are other common methods. The emergency case scenario is evaluated as the highest communication cost, with 45,760/20 * 10 −6 = 0.9152 ms at 20Mbps bandwidth [50].…”

Section: Performance Estimationmentioning

confidence: 99%

“…Attribute-based Encryption [2, 11, 38, 39, 41-43, 43, 44, 48, 51] ( Hybrid Encryption [50] (1) Key generation = 3T 0 +T t , Data decryption = T r , 2014 Multivalued encryption [20] (1) Coverage time= CTFBT ≥ 1 1− ðþ N−1 and 'good, ' respectively [52]. In AYA [44], the efficiency analysis of C p-ABE algorithms is by comparison, using the Jpair library in terms of set time (ms), encryption time, and decryption time. The quantitative measurement entropy of multibiometricbased scheme compared with ECG-and EEG-based schemes shows a high entropy, meaning high security [37].…”

Section: Techniquesmentioning

confidence: 99%

“…Presently, no comprehensive and organized study is available to address patient data privacy and security in S-CI. The literature shows some significant solutions in different application areas such as mobile healthcare [37][38][39][40][41], electronic healthcare [42][43][44][45], health data management [2], and health data aggregation [46]. Hence, the following are the major contributions summarized in this study:…”

Section: Introductionmentioning

confidence: 99%

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Towards Smart Healthcare: Patient Data Privacy and Security in Sensor‐Cloud Infrastructure

Masood

Wang

Daud

et al. 2018

Wireless Communications and Mobile Computing

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Nowadays, wireless body area networks (WBANs) systems have adopted cloud computing (CC) technology to overcome limitations such as power, storage, scalability, management, and computing. This amalgamation of WBANs systems and CC technology, as sensor-cloud infrastructure (S-CI), is aiding the healthcare domain through real-time monitoring of patients and the early diagnosis of diseases. Hence, the distributed environment of S-CI presents new threats to patient data privacy and security. In this paper, we review the techniques for patient data privacy and security in S-CI. Existing techniques are classified as multibiometric key generation, pairwise key establishment, hash function, attribute-based encryption, chaotic maps, hybrid encryption, Number Theory Research Unit, Tri-Mode Algorithm, Dynamic Probability Packet Marking, and Priority-Based Data Forwarding techniques, according to their application areas. Their pros and cons are presented in chronological order. We also provide our six-step generic framework for patient physiological parameters (PPPs) privacy and security in S-CI: (1) selecting the preliminaries; (2) selecting the system entities; (3) selecting the technique; (4) accessing PPPs; (5) analysing the security; and (6) estimating performance. Meanwhile, we identify and discuss PPPs utilized as datasets and provide the performance evolution of this research area. Finally, we conclude with the open challenges and future directions for this flourishing research area.

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Section: E-healthcarementioning

confidence: 99%