Biblio

The cutting-edge biometric recognition systems extract distinctive feature vectors of biometric samples using deep neural networks to measure the amount of (dis-)similarity between two biometric samples. Studies have shown that personal information (e.g., health condition, ethnicity, etc.) can be inferred, and biometric samples can be reconstructed from those feature vectors, making their protection an urgent necessity. State-of-the-art biometrics protection solutions are based on homomorphic encryption (HE) to perform recognition over encrypted feature vectors, hiding the features and their processing while releasing the outcome only. However, this comes at the cost of those solutions' efficiency due to the inefficiency of HE-based solutions with a large number of multiplications; for (dis-)similarity measures, this number is proportional to the vector's dimension. In this paper, we tackle the HE performance bottleneck by freeing the two common (dis-)similarity measures, the cosine similarity and the squared Euclidean distance, from multiplications. Assuming normalized feature vectors, our approach pre-computes and organizes those (dis-)similarity measures into lookup tables. This transforms their computation into simple table-lookups and summation only. We study quantization parameters for the values in the lookup tables and evaluate performances on both synthetic and facial feature vectors for which we achieve a recognition performance identical to the non-tabularized baseline systems. We then assess their efficiency under HE and record runtimes between 28.95ms and 59.35ms for the three security levels, demonstrating their enhanced speed.

Face recognition is a biometric technique that uses a computer or machine to facilitate the recognition of human faces. The advantage of this technique is that it can detect faces without direct contact with the device. In its application, the security of face recognition data systems is still not given much attention. Therefore, this study proposes a technique for securing data stored in the face recognition system database. It implements the Viola-Jones Algorithm, the Kanade-Lucas-Tomasi Algorithm (KLT), and the Principal Component Analysis (PCA) algorithm by applying a database security algorithm using XOR encryption. Several tests and analyzes have been performed with this method. The histogram analysis results show no visual information related to encrypted images with plain images. In addition, the correlation value between the encrypted and plain images is weak, so it has high security against statistical attacks with an entropy value of around 7.9. The average time required to carry out the introduction process is 0.7896 s.

Cancelable biometric is a new era of technology that deals with the protection of the privacy content of a person which itself helps in protecting the identity of a person. Here the biometric information instead of being stored directly on the authentication database is transformed into a non-invertible coded format that will be utilized for providing access. The conversion into an encrypted code requires the provision of an encryption key from the user side. Both invertible and non-invertible coding techniques are there but non-invertible one provides additional security to the user. In this paper, a non-invertible cancelable biometric method has been proposed where the biometric image information is canceled and encoded into a code using a user-provided encryption key. This code is generated from the image histogram after continuous bin updation to the maximal value and then it is encrypted by the Hill cipher. This code is stored on the database instead of biometric information. The technique is applied to a set of retinal information taken from the Indian Diabetic Retinopathy database.

In healthcare 4.0 ecosystems, authentication of healthcare information allows health stakeholders to be assured that data is originated from correct source. Recently, biometric based authentication is a preferred choice, but as the templates are stored on central servers, there are high chances of copying and generating fake biometrics. An adversary can forge the biometric pattern, and gain access to critical health systems. Thus, to address the limitation, the paper proposes a scheme, PHBio, where an encryption-based biometric system is designed prior before storing the template to the server. Once a user provides his biometrics, the authentication process does not decrypt the data, rather uses a homomorphic-enabled Paillier cryptosystem. The scheme presents the encryption and the comparison part which is based on euclidean distance (EUD) strategy between the user input and the stored template on the server. We consider the minimum distance, and compare the same with a predefined threshold distance value to confirm a biometric match, and authenticate the user. The scheme is compared against parameters like accuracy, false rejection rates (FARs), and execution time. The proposed results indicate the validity of the scheme in real-time health setups.

Advanced Encryption Standard (AES) algorithm plays an important role in a data security application. In general S-box module in AES will give maximum confusion and diffusion measures during AES encryption and cause significant path delay overhead. In most cases, either L UTs or embedded memories are used for S- box computations which are vulnerable to attacks that pose a serious risk to real-world applications. In this paper, implementation of the composite field arithmetic-based Sub-bytes and inverse Sub-bytes operations in AES is done. The proposed work includes an efficient multiple round AES cryptosystem with higher-order transformation and composite field s-box formulation with some possible inner stage pipelining schemes which can be used for throughput rate enhancement along with path delay optimization. Finally, input biometric-driven key generation schemes are used for formulating the cipher key dynamically, which provides a higher degree of security for the computing devices.

Efficient large-scale biometric identification is a challenging open problem in biometrics today. Adding biometric information protection by cryptographic techniques increases the computational workload even further. Therefore, this paper proposes an efficient and improved use of coefficient packing for homomorphically protected biometric templates, allowing for the evaluation of multiple biometric comparisons at the cost of one. In combination with feature dimensionality reduction, the proposed technique facilitates a quadratic computational workload reduction for biometric identification, while long-term protection of the sensitive biometric data is maintained throughout the system. In previous works on using coefficient packing, only a linear speed-up was reported. In an experimental evaluation on a public face database, efficient identification in the encrypted domain is achieved on off-the-shelf hardware with no loss in recognition performance. In particular, the proposed improved use of coefficient packing allows for a computational workload reduction down to 1.6% of a conventional homomorphically protected identification system without improved packing.

Considered sensitive information by the ISO/IEC 24745, biometric data should be stored and used in a protected way. If not, privacy and security of end-users can be compromised. Also, the advent of quantum computers demands quantum-resistant solutions. This work proposes the use of Kyber and Saber public key encryption (PKE) algorithms together with homomorphic encryption (HE) in a face recognition system. Kyber and Saber, both based on lattice cryptography, were two finalists of the third round of NIST post-quantum cryptography standardization process. After the third round was completed, Kyber was selected as the PKE algorithm to be standardized. Experimental results show that recognition performance of the non-protected face recognition system is preserved with the protection, achieving smaller sizes of protected templates and keys, and shorter execution times than other HE schemes reported in literature that employ lattices. The parameter sets considered achieve security levels of 128, 192 and 256 bits.

In this paper, an overall introduction of fingerprint encryption algorithm is made, and then a fingerprint encryption algorithm with error correction is designed by adding error correction mechanism. This new fingerprint encryption algorithm can produce stochastic key in the form of multinomial coefficient by using the binary system sequencer, encrypt fingerprint, and use the Lagrange difference value to restore the multinomial during authenticating. Due to using the cyclic redundancy check code to find out the most accurate key, the accuracy of this algorithm can be ensured. Experimental result indicates that the fuzzy vault algorithm with error correction can well realize the template protection, and meet the requirements of biological information security protection. In addition, it also indicates that the system's safety performance can be enhanced by chanaing the key's length.

When storing face biometric samples in accordance with ISO/IEC 19794 as JPEG2000 encoded images, it is necessary to encrypt them for the sake of users’ privacy. Literature suggests selective encryption of JPEG2000 images as fast and efficient method for encryption, the trade-off is that some information is left in plaintext. This could be used by an attacker, in case the encrypted biometric samples are leaked. In this work, we will attempt to utilize a convolutional neural network to perform cryptanalysis of the encryption scheme. That is, we want to assess if there is any information left in plaintext in the selectively encrypted face images which can be used to identify the person. The chosen approach is to train CNNs for biometric face recognition not only with plaintext face samples but additionally conduct a refinement training with partially encrypted data. If this system can successfully utilize encrypted face samples for biometric matching, we can show that the information left in encrypted biometric face samples is information actually usable for biometric recognition.The method works and we can show that a supposedly secure biometric sample still contains identifying information on average over the whole database.

Biometric security is the fastest growing area that receives considerable attention over the past few years. Digital hiding and encryption technologies provide an effective solution to secure biometric information from intentional or accidental attacks. Visual cryptography is the approach utilized for encrypting the information which is in the form of visual information for example images. Meanwhile, the biometric template stored in the databases are generally in the form of images, the visual cryptography could be employed effectively for encrypting the template from the attack. This study develops a share creation with improved encryption process for secure biometric verification (SCIEP-SBV) technique. The presented SCIEP-SBV technique majorly aims to attain security via encryption and share creation (SC) procedure. Firstly, the biometric images undergo SC process to produce several shares. For encryption process, homomorphic encryption (HE) technique is utilized in this work. To further improve the secrecy, an improved bald eagle search (IBES) approach was exploited in this work. The simulation values of the SCIEP-SBV system are tested on biometric images. The extensive comparison study demonstrated the improved outcomes of the SCIEP-SBV technique over compared methods.

Mobile healthcare (mHealth) application and technologies have promised their cost-effectiveness to enhance healthcare quality, particularly in rural areas. However, the increased security incidents and leakage of patient data raise the concerns to address security risks and privacy issues of mhealth applications urgently. While recent mobile health applications that rely on password-based authentication cannot withstand password guessing and cracking attacks, several countermeasures such as One-Time Password (OTP), grid-based password, and biometric authentication have recently been implemented to protect mobile health applications. These countermeasures, however, can be thwarted by brute force attacks, man-in-the-middle attacks and persistent malware attacks. This paper proposed grid-based honey encryption by hybridising honey encryption with grid-based authentication. Compared to recent honey encryption limited in the hardening password attacks process, the proposed grid-based honey encryption can be further employed against shoulder surfing, smudge and replay attacks. Instead of rejecting access as a recent security defence mechanism in mobile healthcare applications, the proposed Grid-based Honey Encryption creates an indistinct counterfeit patient's record closely resembling the real patients' records in light of each off-base speculation legitimate password.

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.

The theoretical basis made it possible to implement software for automated secure biometric verification and personal identification, which can be used by information security systems (including access control and management systems). The work is devoted to solving an urgent problem - the development of methods and algorithms for generating a key for a storage device based on biometric parameters. Biometric cryptosystems take advantage of biometrics to improve the security of encryption keys. The ability not to store a key that is derived from biometric data is a direct advantage of the method of generating cryptographic keys from biometric data of users over other existing encryption methods.

Biometric matching involves storing and processing sensitive user information. Maintaining the privacy of this data is thus a major challenge, and homomorphic encryption offers a possible solution. We propose a privacy-preserving biometrics-based authentication protocol based on fully homomorphic en-cryption, where the biometric sample for a user is gathered by a local device but matched against a biometric template by a remote server operating solely on encrypted data. The design ensures that 1) the user's sensitive biometric data remains private, and 2) the user and client device are securely authenticated to the server. A proof-of-concept implementation building on the TFHE library is also presented, which includes the underlying basic operations needed to execute the biometric matching. Performance results from the implementation show how complex it is to make FHE practical in this context, but it appears that, with implementation optimisations and improvements, the protocol could be used for real-world applications.

Biometric data is prone to attacks and threats from hackers who are professionals in cyber-crimes. Therefore, securing the data is very essential. Steganographic approach, which is a process of concealing data, is proposed as a solution to this. Biometrics are hidden inside other biometrics for safe storage and secure transmission. Also, it is designed to be robust against attacks, and cannot be detected easily. The intention of this paper is to highlight a method of hiding one image in another image by using mid position value(mpv) technique. Here we have to choose the secret biometric on which Arnold transform will be applied resulting in a scrambled version of the secret biometric. This will be enveloped inside cover image which results in a stego-image. Lastly, hidden secret biometric will be decoded from this stego image, which will first result in a scrambled secret biometric. Inverse Arnold Transform will be applied on this to finally result in the decoded secret biometric. The paper further explains the working and processes in detail.

This paper considers methods of fingerprint protection in biometric authentication systems. Including methods of protecting fingerprint templates using zero digital watermarks and cryptography techniques. The paper considers a secure authentication model using cryptography and digital watermarks.

Nowadays, face recognition is used everywhere in all fields. Though the face recognition is used for security purposes there is also chance in hacking the faces which is used for face recognition. For enhancing the face security, encryption and decryption technique is used. Face cognizance has been engaged in more than a few security-connected purposes such as supervision, e-passport, and etc… The significant use of biometric raises vital private concerns, in precise if the biometric same method is carried out at a central or unfrosted servers, and calls for implementation of Privacy improving technologies. For privacy concerns the encoding and decoding is used. For achieving the result we are using the Open Computer Vision (OpenCV) tool. With the help of this tool we are going to cipher the face and decode the face with advanced encryption standards techniques. OpenCV is the tool used in this project

Reversible data hiding (RDH) is an emerging area in the field of information security. The RDH schemes are widely explored in the field of cloud computing for data authentication and in medical image transmission for clinical data transmission along with medical images. The RDH schemes allow the data hider to embed sensitive information in digital content in such a way that later it can be extracted while recovering the original image. In this research, we explored the use of the RDH through the encryption scheme in a biometric authentication system. The internet of things (IoT) enabled biometric authentication systems are very common nowadays. In general, in biometric authentication, computationally complex tasks such as feature extraction and feature matching will be performed in a cloud server. The user-side devices will capture biometric data such as the face, fingerprint, or iris and it will be directly communicated to the cloud server for further processing. Since the confidentiality of biometric data needs to be maintained during the transmission, the original biometric data will be encrypted using any one of the data encryption techniques. In this manuscript, we propose the use of RDH through encryption approach to transmit two different biometric data as a single file without compromising confidentiality. The proposed scheme will ensure the integrity of the biometric data during transmission. For data hiding purposes, we have used a block-wise RDH through encryption scheme. The experimental study of the proposed scheme is carried out by embedding fingerprint data in the face images. The validation of the proposed scheme is carried out by extracting the fingerprint details from the face images during image decryption. The scheme ensures the exact recovery of face image images and fingerprint data at the receiver site.

When biometric databases grow larger, a security breach or leak can affect millions. In order to protect against such a threat, the use of encryption is a natural choice. However, a biometric identification attempt then requires the decryption of a potential huge database, making a traditional approach potentially unfeasible. The use of selective JPEG2000 encryption can reduce the encryption’s computational load and enable a secure storage of biometric sample data. In this paper we will show that selective encryption of face biometric samples is secure. We analyze various encoding settings of JPEG2000, selective encryption parameters on the "Labeled Faces in the Wild" database and apply several traditional and deep learning based face recognition methods.

Biometric methods are the most effective and accurate authentication methods. However, a significant drawback of such methods is the storage of authentication information in clear text. The article is devoted to solving this problem by means of symmetric encryption method and the method of dividing the memory space. The method of symmetric encryption ensures confidentiality during storage and transmission of biometric characteristics, the method of dividing the memory space provides an increase of information security level during processing of biometric characteristics.

The facial recognition time by time takes more importance, due to the extend kind of applications it has, but it is still challenging when faces big variations in the characteristics of the biometric data used in the process and especially referring to the transportation of information through the internet in the internet of things context. Based on the systematic review and rigorous study that supports the extraction of the most relevant information on this topic [1], a software architecture proposal which contains basic security requirements necessary for the treatment of the data involved in the application of facial recognition techniques, oriented to an IoT environment was generated. Concluding that the security and privacy considerations of the information registered in IoT devices represent a challenge and it is a priority to be able to guarantee that the data circulating on the network are only accessible to the user that was designed for this.

Individualization of anonymous identities using artificial intelligence - enables innovative human-computer interaction through the personalization of communication which is, at the same time, individual and anonymous. This paper presents possible approach for individualization of anonymous identities in real time. It uses computer vision and artificial intelligence to automatically detect and recognize person's age group, gender, human body measures, proportions and other specific personal characteristics. Collected data constitutes the so-called person's biometric footprint and are linked to a unique (but still anonymous) identity that is recorded in the computer system, along with other information that make up the profile of the person. Identity anonymization can be achieved by appropriate asymmetric encryption of the biometric footprint (with no additional personal information being stored) and integrity can be ensured using blockchain technology. Data collected in this manner is GDPR compliant.

The rapid proliferation of biometrics has led to growing concerns about the security and privacy of the biometric data (template). A biometric uniquely identifies an individual and unlike passwords, it cannot be revoked or replaced since it is unique and fixed for every individual. To address this problem, many biometric template protection methods using fully homomorphic encryption have been proposed. But, most of them (i) are computationally expensive and practically infeasible (ii) do not support operations over real valued biometric feature vectors without quantization (iii) do not support packing of real valued feature vectors into a ciphertext (iv) require multi-shot enrollment of users for improved matching performance. To address these limitations, we propose a secure and privacy preserving method for biometric template protection using fully homomorphic encryption. The proposed method is computationally efficient and practically feasible, supports operations over real valued feature vectors without quantization and supports packing of real valued feature vectors into a single ciphertext. In addition, the proposed method enrolls the users using one-shot enrollment. To evaluate the proposed method, we use three face datasets namely LFW, FEI and Georgia tech face dataset. The encrypted face template (for 128 dimensional feature vector) requires 32.8 KB of memory space and it takes 2.83 milliseconds to match a pair of encrypted templates. The proposed method improves the matching performance by 3 % when compared to state-of-the-art, while providing high template security.

The biometric system of access to information resources has been developed. The software and hardware complex are designed to protect information resources and personal data from unauthorized access using the principle of user authentication by fingerprints. In the developed complex, the traditional input of login and password was replaced by applying a finger to the fingerprint scanner. The system automatically recognizes the fingerprint and provides access to the information resource, provides encryption of personal data and automation of the authorization process on the web resource. The web application was implemented using the Bootstrap framework, the 000webhost web server, the phpMyAdmin database server, the PHP scripting language, the HTML hypertext markup language, along with cascading style sheets and embedded scripts (JavaScript), which created a full-fledged web-site and Google Chrome extension with the ability to integrate it into other systems. The structural schematic diagram was performed. The design of the device is offered. The algorithm of the program operation and the program of the device operation in the C language are developed.

The use of biometrics in security applications may be vulnerable to several challenges of hacking. Thus, the emergence of cancellable biometrics becomes a suitable solution to this problem. This paper presents a one-way cancellable biometric transform that depends on 3D chaotic maps for face and fingerprint encryption. It aims to avoid cloning of original biometrics and allow the templates used by each user in different applications to be variable. The permutations achieved with the chaotic maps guarantee high security of the biometric templates, especially with the 3D implementation of the encryption algorithm. In addition, the paper presents a hardware implementation for this framework. The proposed algorithm also achieves good performance in the presence of low and moderate levels of noise. An experimental version of the proposed cancellable biometric system has been applied on FPGA model. The obtained results achieve a powerful performance of the proposed cancellable biometric system.