Abstract:Most current security and authentication systems are based on personal biometrics. The security problem is a major issue in the field of biometric systems. This is due to the use in databases of the original biometrics. Then biometrics will forever be lost if these databases are attacked. Protecting privacy is the most important goal of cancelable biometrics. In order to protect privacy, therefore, cancelable biometrics should be non-invertible in such a way that no information can be inverted from the cancela… Show more
“…Other cancelable biometric systems are presented in [7,8]. The authors of [7] provided a cancelable method for speech recognition based on encryption algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…An additional encryption algorithm is implemented using double random phase encoding (DRPE) to increase the security level of the encrypted signals. The proposed methodology was improved in [8] to protect the privacy of biometric data using cancelable biometrics where the data cannot be inverted so that the user information is kept private and secured. The authors suggested a hybrid algorithm based on the Fourier transform and Jigsaw transform.…”
Many types of research focus on utilizing Palmprint recognition in user identification and authentication. The Palmprint is one of biometric authentication (something you are) invariable during a person's life and needs careful protection during enrollment into different biometric authentication systems. Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification. This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users. The HAMTE is generated for each user during the enrollment phase, which is responsible for generating a secure template for the enrolled user. The proposed network secures the person's Palmprint template by translating it into an irreversible template (different features space). It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person's template from being stolen. Experimental results are conducted on the CASIA database, where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates. The recognition accuracy deviated by around 3%, and the equal error rate (EER) by approximately 0.02 compared to the original data, with appropriate performance (approximately 13 ms) while preserving the irreversibility property of the secure template. Moreover, the brute-force attack has been analyzed under the new Palmprint protection scheme.
“…Other cancelable biometric systems are presented in [7,8]. The authors of [7] provided a cancelable method for speech recognition based on encryption algorithms.…”
Section: Related Workmentioning
confidence: 99%
“…An additional encryption algorithm is implemented using double random phase encoding (DRPE) to increase the security level of the encrypted signals. The proposed methodology was improved in [8] to protect the privacy of biometric data using cancelable biometrics where the data cannot be inverted so that the user information is kept private and secured. The authors suggested a hybrid algorithm based on the Fourier transform and Jigsaw transform.…”
Many types of research focus on utilizing Palmprint recognition in user identification and authentication. The Palmprint is one of biometric authentication (something you are) invariable during a person's life and needs careful protection during enrollment into different biometric authentication systems. Accuracy and irreversibility are critical requirements for securing the Palmprint template during enrollment and verification. This paper proposes an innovative HAMTE neural network model that contains Hetero-Associative Memory for Palmprint template translation and projection using matrix multiplication and dot product multiplication. A HAMTE-Siamese network is constructed, which accepts two Palmprint templates and predicts whether these two templates belong to the same user or different users. The HAMTE is generated for each user during the enrollment phase, which is responsible for generating a secure template for the enrolled user. The proposed network secures the person's Palmprint template by translating it into an irreversible template (different features space). It can be stored safely in a trusted/untrusted third-party authentication system that protects the original person's template from being stolen. Experimental results are conducted on the CASIA database, where the proposed network achieved accuracy close to the original accuracy for the unprotected Palmprint templates. The recognition accuracy deviated by around 3%, and the equal error rate (EER) by approximately 0.02 compared to the original data, with appropriate performance (approximately 13 ms) while preserving the irreversibility property of the secure template. Moreover, the brute-force attack has been analyzed under the new Palmprint protection scheme.
“…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]. Ratha et al [14] proposed a method for identification based on a cancelable geometric fingerprint framework.…”
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.
“…18,19 Addressing this gap, our research introduces a novel optical hashing and compression paradigm, ingeniously leveraging the SWIFFT family of post-quantum hashing algorithms. [20][21][22][23][24][25][26][27][28][29][30] This innovative approach marks a significant leap forward, eschewing the conventionally employed slow, high-resolution CMOS cameras in favor of ultra-fast, signal-triggered CMOS detector arrays. The transition to these advanced detector arrays catalyzes a remarkable acceleration in information acquisition rates.…”
Section: Introductionmentioning
confidence: 99%
“…As computational power continues to scale, particularly with the looming advent of quantum computing, traditional cryptographic methods face potential obsolescence. [62][63][64][65][66][67][68][69] In this context, post-quantum hashing algorithms stand as a bulwark against future threats, ensuring that our data remains secure against even the most advanced computational attacks. 70,71 The implementation of this optical hashing and compression strategy engenders a suite of enhancements across the computational spectrum.…”
Here, we introduce a framework leveraging a free-space optical system based on the 4f configuration, inspired by the SWIFFT algorithms, designed to significantly enhance the processing of encrypted data using analog optical AI accelerators. This innovative approach aims to outperform traditional electronic machine learning accelerators by offering superior processing speeds and heightened data security. The adoption of the 4f-based optical setup is pivotal, harnessing the natural Fourier transform capabilities of light to efficiently implement complex mathematical operations inherent in hashing and compression algorithms. This alignment with the Fourier transform properties optimizes the system's computational throughput and energy efficiency, far surpassing conventional electronic processing methods. Furthermore, the utilization of analog optical AI accelerators within this 4f configuration underscores a groundbreaking advancement in processing encrypted data securely and swiftly. The 4f system facilitates parallel processing and high-speed data transmission, characteristics that are instrumental in reducing latency and enhancing the security measures of data processing.
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