Privacy and Security Concerns in Edge Computing-Based Smart Cities

Kumar, Ashutosh; Upadhyay, Aditya; Mishra, Neha; Nath, Srawan; Yadav, Kalu Ram; Sharma, Gajanand

doi:10.1007/978-3-030-96737-6_5

Cited by 5 publications

(3 citation statements)

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“…A generic biometric system's functions include capturing and storing enrollment (reference) biometric samples, capturing new ones, and comparing them to related reference samples. Given specific systems, Kumar et al 5 will differ in their specifics; Figure 1 Enrolment operations: To enroll a subject region in a fingerprint scanner, the functions defined in Illustrates A, B, C, and E are still used in higher processes. Saravanan et al 6 proposed the materials are assessed to determine that their integrity satisfies the detection algorithm that will be used to compare them to samples obtained in the field.…”

Section: Basic Biometric System Operationsmentioning

confidence: 99%

“…Let z(0) = j(1) and the first data dispersion direction. This IPCA formula, according to (5), provides a first estimate of its exploratory factor j(1), which corresponds to a new and highest eigenvalue:…”

Section: Feature Extraction Of Incremental Principal Component Analysismentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

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Secure personal authentication in fog devices via multimodal rank‐level fusion

Selvaraj

Thangavel

2023

Concurrency and Computation

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Summary In recent years, Fog and edge computing have gained enormous increases and usage of Internet of Things (IoT) devices in research and development even though it provides challenges such as security and privacy. One way to ensure this is a fusion of multimodal biometrics authentication systems, which offer highly reliable biometric authentication solutions to reduce the failure to enroll rate, increase the degree of freedom, and deter spoof attacks. Since unimodal biometric systems often face significant limitations due to sensitivity to noise, data quality, non‐universality, and illumination variations. In this research, we propose management of access control to ensure the desired level of security, a Rank level fusion integration method. ORL, GT, CASIA, and VGG‐face databases were used for testing and evaluating the implemented system. Thus, using the LDA with SCE based feature selection approach, the average GAR percent for the facial, hearing, and palm vein is 3.25% for the facial and muffs pulse and 8.55% for the head and palm vein result was the overall performance of the biometric system even in the presence of low‐quality data in the Fog environment.

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Section: Basic Biometric System Operationsmentioning

confidence: 99%

Section: Feature Extraction Of Incremental Principal Component Analysismentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

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