Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Sedik, Ahmed; El‐Latif, Ahmed A. Abd; Wani, Mudasir Ahmad; El‐Samie, Fathi E. Abd; Bauomy, Nariman Abdel‐Salam; Hashad, Fatma G.

doi:10.3390/math11030703

Cited by 8 publications

(4 citation statements)

References 39 publications

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“…1. Logistic map [27,28]: The first 1,000 elements (x-999…x0) are ignored due to the transient period. If n > 0, then the time series is calculated for xn.…”

Section: Generation Of Synthetic Time Seriesmentioning

confidence: 99%

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

Conejero,

Velichko,

Garibo-i-Orts

et al. 2023

Preprint

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In this paper, we propose a method for assessing the effectiveness of entropy features using several chaotic mappings. We anlyze fuzzy entropy (FuzzyEn) and neural network entropy (NNetEn) on four discrete mappings: the logistic map, the sine map, the Planck map, and the two-memristor-based map, with a base length time series of 300 elements. FuzzyEn is shown to have improved global efficiency (GEFMCC) in the classification task compared to NNetEn. At the same time, there are local areas of the time series dynamics in which the classification efficiency NNetEn is higher than FuzzyEn. The results of using horizontal visibility graphs (HVG) instead of the raw time series are also shown. GEFMCC decreases after HVG time series transformation, but there are local areas of the time series dynamics in which the classification efficiency increases after including the HVG. The scientific community can use the results to explore the efficiency of entropy-based classification of time series.

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“…1. Logistic map [27,28]: The first 1,000 elements (x-999…x0) are ignored due to the transient period. If n > 0, then the time series is calculated for xn.…”

Section: Generation Of Synthetic Time Seriesmentioning

confidence: 99%

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

Conejero,

Velichko,

Garibo-i-Orts

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…The design of cancelable biometric transforms makes the recovery of the original biometric data a computationally hard process [8,9]. Several studies have been presented to generate cancelable biometrics [10][11][12][13].…”

Section: Related Workmentioning

confidence: 99%

A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication

et al. 2023

Self Cite

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In recent times, there has been a noticeable increase in the application of human biometrics for user authentication in various domains, such as online banking. However, the use of biometric systems poses security risks and the potential for misuse, primarily due to the storage of original templates in databases. To tackle this issue, the concept of cancelable biometrics has emerged as a reliable method utilizing one-way encryption. Several algorithms have been developed to implement cancelable biometrics, incorporating visual representations of single or multiple biometrics. This research proposes a cancelable biometric system that utilizes deep learning techniques to generate two encrypted modalities, namely text and image, using facial and fingerprint biometrics acquired from a smartphone. The system consists of two main stages: a visual encoder and a text encoder. The visual encoder converts the fingerprint style into a facial representation, creating a cancelable template to ensure the potential for cancelation. The resulting visual template is then processed by the text encoder, which employs hashing techniques to generate a corresponding text template. User authentication is automatically verified by utilizing the generated templates through Siamese networks.

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“…In collaborative learning, some people might act dishonestly by giving false information or doing things wrong, either to keep data private or for selfish reasons [1,2]. There are many sensitive pieces of data created by social activities, like network activities, travel data, electronic health records, financial data, and personal information [3,4].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation

Siyal,

Long,

Asim

et al. 2024

Mathematics

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Ensuring data confidentiality is a critical requirement for modern security systems globally. Despite the implementation of various access-control policies to enhance system security, significant threats persist due to insecure and inadequate access management. To address this, Multi-Party Authorization (MPA) systems employ multiple authorities for authorization and authentication, utilizing blockchain technology to store and access data securely, ensuring immutable and trusted audit trails. In this work, we propose a hybrid key-generation approach called the Identity and Attribute-Based Honey Encryption (IABHE) Algorithm combined with Deep Spiking Neural Network (DSNN) denoted by IABHE+DSNN for secure data sharing in a multi-party blockchain-based system. This approach incorporates various entities and multiple security functionalities to ensure data security. The data-sharing process involves several steps: initialization, authentication, initial registration, data protection, validation, and data sharing. Data protection is executed within the MapReduce framework, with data encryption performed using IABHE and key generation managed by DSNN. Experimental results demonstrate that the proposed IABHE+DSNN approach achieves a decryption time of 10.786 s, an encryption time of 15.765 s, and a key complexity of 0.887, outperforming existing methods.

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Efficient Multi-Biometric Secure-Storage Scheme Based on Deep Learning and Crypto-Mapping Techniques

Cited by 8 publications

References 39 publications

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

Exploring Entropy-Based Classification of Time Series Using Visibility Graphs from Chaotic Maps

A Cancelable Biometric System Based on Deep Style Transfer and Symmetry Check for Double-Phase User Authentication

Blockchain-Enabled Secure Data Sharing with Honey Encryption and DSNN-Based Key Generation

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