CloudID: Trustworthy cloud-based and cross-enterprise biometric identification

Haghighat, Mohammad; Zonouz, Saman; Abdel-Mottaleb, Mohamed

doi:10.1016/j.eswa.2015.06.025

Cited by 273 publications

(120 citation statements)

References 38 publications

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“…To make more specifical comparison, we compare the 3D-Gabor-IEUE with other view generation and query selection methods as well. The comparison of view generation approaches is conducted using spectral information, 2D-Gabor [54] and the 3D-Gabor construction with no cube assessment named 3D-Gabor(no CA). The comparison of query selection is performed by maximum disagreement (MD) [34], entropy query-by-bagging (EQB) [24], adaptive maximum disagreement (AMD) [41], breaking ties (BT(SV)) [43] and randomly sampling (RS(SV)).…”

Section: Methodsmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

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Active learning (AL) has been shown to be very effective in hyperspectral image (HSI) classification. It significantly improves the performance by selecting a small quantity of the most informative training samples to reduce the complexity of classification. Multiview AL (MVAL) can make the comprehensive analysis of both object characterization and sampling selection in AL by using various features of multiple views. However, the original MVAL cannot effectively exploit the spectral-spatial information by respecting the three-dimensional (3D) nature of the HSI and the query selection strategy in the MVAL is only based on the disagreement of multiple views. In this paper, we propose a 3D-Gabor inspired MVAL method for spectral-spatial HSI classification, which consists of two main steps. First, in the view generation step, we adopt a 3D-Gabor filter to generate multiple cubes with limited bands and utilize the feature assessment strategies to select cubes for constructing views. Second, in the sampling selection step, a novel method is proposed by using both internal and external uncertainty estimation (IEUE) of views. Specifically, we use the distributions of posterior probability to learn the "internal uncertainty" of each independent view, and adopt the inconsistencies between views to estimate the "external uncertainty". Classification accuracies of the proposed method for the four benchmark HSI datasets can be as high as 99.57%, 99.93%, 99.02%, 98.82%, respectively, demonstrating the improved performance as compared with other state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

3D-Gabor Inspired Multiview Active Learning for Spectral-Spatial Hyperspectral Image Classification

Hu¹,

He²,

Li³

et al. 2018

Remote Sensing

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“…As [8] point out, cloud can be employed to provide crossenterprise biometric identification. Therefore, delays in the authentication process can effectively lock out cloud stakeholders, or cause inconveniences.…”

Section: Research Articlementioning

confidence: 99%

Congestion Aware Packet Routing for Delay Sensitive Cloud Communications

Nyangaresi¹,

Abeka²,

Ogara³

2017

ijcna

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-In the recent years, many organizations have turned to cloud technology to support their information technology services.The cloud servers are therefore increasingly holding huge and sensitive information belonging to diverse groups of individuals and companies. Additionally, some organizations employ the cloud to provide them with online backup services. One of the most outstanding requirements for cloud customers is availability -the customers must be able to access their information and other resources stored in the cloud any time and from anywhere on the globe. This means that there should be efficient network design such that any delays are averted. The connection between the customer and the cloud can therefore be regarded as delay senstive. Network congestions often lead to delays and packet losses. Transmission control protocol employs four congestion control algorithms -slow start, congestion avoidance, fast retransmit and fast recovery, all of which fail to meet the requirements of delay intolerance. Transmission control protocol pacing has been suggested as a possible solution to delays and packet dropping in computer networks. However, the conventional pacing is static in nature, meaning that constant pauses are introduced between packet transmissions to prevent bursty transmissions which can lead to delays at the receiver buffers. This paper therefore presents a congestion aware packet routing where the delay period is hinged on the prevailing network conditions. This dynamic pacing algorithm was designed and implemented in Spyder using Python programming language. It employed probe signals to gather network intelligence such as the applicable round trip times of the network. Thereafter, this network intelligence was employed to tailor the paces to these network conditions. The results obtained showed that this algorithm introduced longer paces when more packets are transmitted and shorter paces when few packets are transmitted. In so doing, this new algorithm gives enough time for large packets to be delivered and smaller paces when few packets are sent. The analysis was done in terms of bandwidth utilization efficiency, round trip times and congestion window size adjustments. The congestion window -time graphs and throughput -time graphs showed that the developed dynamic pacing algorithm adjusted quickly to network congestions hence ensuring that the network is efficiently utilized by averting delays.

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“…For the management of fleet, vehicular data and road safety innumerable number of telematics units, smart telephones and RFID tags were used in urban areas for the effective management of logistics and supply chain management [4,5]. Cloud computing and storage solutions provide users and enterprises with various capabilities to store and process their data in either privately owned, or third-party data centers that may be located far from the user-ranging in distance from across a city to across the world [6]. The goal of cloud computing is to allow users to take benefit from all of these technologies, without the need for deep knowledge about or expertise with each one of them.…”

Section: Literature Reviewmentioning

confidence: 99%