Biometric Cryptosystems Research Articles

A Robust Multimodel Biometric Crypto System

Biometric based authentication has several advantages over traditional password or PIN based authentication process because biometric is consists of physical or behavioural characteristics i.e fingerprint, face, Finger Knuckle Print (FKP), iris, voice etc. Unimodal biometric system h as some drawbacks i.e non universality, inter-class variation, intra-class variation; system can be circumvented by the skilled imposter etc. These drawbacks can overcome by multimodal biometric system as it combines more than one modality for authentication. When multimodal system combined with cryptography it makes system more robust and secure. In this paper, a robust multimodal biometric crypto system has been proposed, in which two modalities (FKP and face) are used for authentication of a person and one modality (fingerprint) is used for key generation. AES algorithm with fingerprint based key is used for securing the biometric templates. At authentication time, decision level fusion with AND rule is used for making the final decision. The proposed multimodal biometric crypto system is more robust and secure as compare with other multimodal biometric systems. Experimental results are shown with the help of MATLAB3. 2017b.

A fingerprint based crypto-biometric system for secure communication

To maintain secrecy of information during communication, cryptography is considered to be an impressive solution and cryptographic keys play an important role to ensure the security. However, these randomly derived keys (of 256 bits) are hard to memorize. Also, there is a threat of privacy invasion since the storage, protection and transmission of a key over a communication link may lead to information leakage. Therefore, researchers propose to utilize user’s biometric trait to generate the cryptographic key in a session-based communication environment. This avoids the storage of cryptographic keys without negotiating on secrecy. The biometric-based key generation encompasses concerns over biometric template protection, biometric data sharing between users and revocable key generation from biometric. To address the aforementioned concerns, we propose a framework for secure communication between two users using a fingerprint-based crypto-biometric system. First, the feature bit-string are computed from the users’ fingerprint. Next, revocable transformation is applied to derive the private keys of respective users. Then, the Diffie–Hellman (DH) algorithm is used to generate public keys from private keys of both sender and receiver, which are shared and further used to produce a symmetric cryptographic key at both ends. Here, the biometric data is neither stored nor shared which ensures the security of biometric data. Also, perfect forward secrecy is achieved using session keys. This work also provides the long-term protection of messages communicated between two users. It is evident from the experimental evaluation over four datasets of FVC2002, four datasets of FVC 2004, and NIST special database IV that the proposed framework is privacy-preserving and could be utilized for real access control systems.

Symmetric keyring encryption scheme for biometric cryptosystem

In this paper, we propose a novel biometric cryptosystem for vectorial biometrics called symmetric keyring encryption (SKE), inspired by Rivest's keyring model (2016). Unlike conventional biometric secret-binding primitives, such as fuzzy commitment and fuzzy vault approaches, the proposed scheme reframes the biometric secret-binding problem as a fuzzy symmetric encryption problem using a concept called a resilient vector pair. In this study, this pair resembles the encryption–decryption key pair in symmetric key cryptosystems. This scheme is realized using an index of maximum hashed vectors, a special instance of the ranking-based locality-sensitive hashing function. With a simple filtering mechanism and an [m, k] Shamir's secret-sharing scheme, we show that SKE, both in theory and in an empirical evaluation, can retrieve the exact secret with overwhelming probability for a genuine input yet negligible probability for an imposter input. Although SKE can be applied to any vectorial biometrics, we adopt fingerprint and face vectors in this work. Experiments were performed using the Fingerprint Verification Competition (FVC) and Labeled Face in the Wild (LFW) datasets. We formalize and analyze the threat model for SKE, which involves several major security attacks.

A New Design for Alignment-Free Chaffed Cancelable Iris Key Binding Scheme

Iris has been found to be unique and consistent over time despite its random nature. Unprotected biometric (iris) template raises concerns in security and privacy, as numerous large-scale iris recognition projects have been deployed worldwide—for instance, susceptibility to attacks, cumbersome renewability, and cross-matching. Template protection schemes from biometric cryptosystems and cancelable biometrics are expected to restore the confidence in biometrics regarding data privacy, given the great advancement in recent years. However, a majority of the biometric template protection schemes have uncertainties in guaranteeing criteria such as unlinkability, irreversibility, and revocability, while maintaining significant performance. Fuzzy commitment, a theoretically secure biometric key binding scheme, is vulnerable due to the inherent dependency of the biometric features and its reliance on error correction code (ECC). In this paper, an alignment-free and cancelable iris key binding scheme without ECC is proposed. The proposed system protects the binary biometric data, i.e., IrisCodes, from security and privacy attacks through a strong and size varying non-invertible cancelable transform. The proposed scheme provides flexibility in system storage and authentication speed via controllable hashed code length. We also proposed a fast key regeneration without either re-enrollment or constant storage of seeds. The experimental results and security analysis show the validity of the proposed scheme.

Improved fuzzy commitment scheme

To ensure privacy and secrecy of biometric data, template protection schemes are widely used. Template protection schemes ensure renewability, irreversibility, and unlinkability among the templates. The Fuzzy Commitment Scheme is one of the widely used template protection schemes. This biometric cryptosystem combines cryptography and error correction codes. The original fuzzy commitment scheme is not secure. In this paper, an improved fuzzy commitment scheme has been introduced. The introduced scheme is validated using biometric data from the CASIA-Iris-Thousand dataset. In this paper, improved fuzzy commitment scheme or code-offset constructions are presented that use more than one key to secure the biometric data. The additional keys increase the exhaustive search space. The additional key made it impossible for an intruder to utilize the decoding algorithms to gain information about the user biometrics.

Effective Combination of Iris-based Cancelable Biometrics and Biometric Cryptosystems

The fuzzy commitment scheme (FCS) is one of the most effective biometric cryptosystems (BCs) that provide secure management of cryptographic keys using biometric templates. In this scheme, error correcting codes (ECCs) are firstly employed to encode a cryptographic key into a codeword which is then secured via linking (committing) it with a biometric template of the same length. Unfortunately, the key length is constrained by the size of the adopted biometric template as well as the employed ECC(s). In this paper, we propose a secure iris template protection scheme that combines cancelable biometrics with the FCS in order to secure long cryptographic keys without sacrificing the recognition accuracy. First, we utilize cancelable biometrics to derive revocable templates of large sizes from the most reliable bits in iris codes. Then, the FCS is applied to the obtained cancelable iris templates to secure cryptographic keys of the desired length. The revocability of cryptographic keys as well as true iris templates is guaranteed due to the hybridization of both techniques. Experimental results show that the proposed hybrid system can achieve high recognition accuracy regardless of the key size.

Comparative Analysis of Crypto Systems Using Biometric Key

In digital world, the security of data is one of the most important issues. Biometric cryptosystems provide great convenience and security by combining biometrics and cryptography. The aim of this paper is to provide a good standpoint into the state-of-the-art and current research directions of crypto systems using biometric key, including theory and applications.

Unlock Your Heart: Next Generation Biometric in Resource-Constrained Healthcare Systems and IoT

With the emergence of the Internet-of-Things, there is a growing need for access control and data protection on low-power, pervasive devices. Key-based biometric cryptosystems are promising for IoT due to its convenient nature and lower susceptibility to attacks. However, the costs associated with biometric processing and template protection are nontrivial for smart cards, and so forth. In this paper, we discuss the cost versus the utility of biometric systems and investigate frameworks for improving them. We propose the noise-aware biometric quantization framework (NA-IOMBA) capable of generating unique, reliable, and high entropy keys with low enrollment times and costs with several experiments. First, we compare its performance with IOMBA and one-class-SVM on multiple biometric modalities, including popular ones (fingerprint and iris) and emerging cardiovascular ones (ECG and PPG). The results show that NA-IOMBA outperforms them all and that ECG provides the best trade-off between reliability, key length, entropy, and implementation cost. Second, we examine the impact on key reliability with ECGs obtained at different sessions and trained with a different number of heartbeats. Finally, implementation results show that incorporating noise models with NA-IOMBA reduces power and utilization overhead by more than 60% by adapting the pre-processing, feature extraction, and post-processing modules.

Convolutional Neural Network Biometric Cryptosystem for the Protection of the Blockchain’s Private Key

Blockchain technology has attracted a lot of attention in the previous years as a secure way to protect transactions in different processes. It has been particularly used to define cryptocurrencies. While inherently secure against classical single node attacks, the blockchain cryptocurrencies have recently been subject to attacks by malwares able to capture a single user wallet and its included keys. In this work we propose the use of biometric cryptosystems to control the access to the wallets on single machines. After a brief description of the blockchain, the cryptocurrencies and the possible attacks, the paper describes the use of convolutional neural network face recognition as a tool to extract biometric features that help in a key binding approach to protect the personal data in the wallet. Experiments have been conducted on three independent face datasets and the results obtained are satisfactory. The equal error rate between false acceptance and false rejection is negligible when testing on images from the same dataset used for the training of the convolutional neural network. This generalizes well when experimenting on two other independent datasets. These results prove that face cryptosystems can be used to protect the access on sensitive data existing in the wallets of many cryptocurrencies.

Construction of a New Biometric-Based Key Derivation Function and Its Application

Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.

New biometric cryptosystem to protect sensitive data in Internet of objects

Ensuring protection of sensitive data in embedded devices widespread and operanting in adversary environment is a major issue. Among lot of instance of this problem, IoT is the well-know case study. In addition, very often for cost reason, the devices used in Internet of Things (IoT) do not integra te secure components (cryptoprocessor, encrypted memory, etc) like smart card. To solve this issue, this paper presents two contributions to fuzzy vault-biometric cryptosystems which can enable IoT devices supporting biometry to secure sensitive data they embed in their memory when they operate in an adverse environment. If an adversary captures a device and read content of this regular memory, it will be very difficult for him to recover the protected data using brute-force. The first advantage of the proposals is thus to enable IoT devices to still embed regular memory, to protect sensitive data, instead of an expensive native encrypted memory or to add a cryptoprocessor. The second advantage of the proposals is to enable to not require storing of helper data and thus improve security and also save memory. Obviously our proposals can also be used in already secure devices to enhance the security level. Experimental results performed using fingerprint modality show that the proposals have the potential to efficiently protect sensitive data despite the strong constrained of IoT devices.

An Artificial Neural Network Framework for Gait-Based Biometrics.

As the popularity of wearable and the implantable body sensor network (BSN) devices increases, there is a growing concern regarding the data security of such power-constrained miniaturized medical devices. With limited computational power, BSN devices are often not able to provide strong security mechanisms to protect sensitive personal and health information, such as one's physiological data. Consequently, many new methods of securing wireless body area networks have been proposed recently. One effective solution is the biometric cryptosystem (BCS) approach. BCS exploits physiological and behavioral biometric traits, including face, iris, fingerprints, electrocardiogram, and photoplethysmography. In this paper, we propose a new BCS approach for securing wireless communications for wearable and implantable healthcare devices using gait signal energy variations and an artificial neural network framework. By simultaneously extracting similar features from BSN sensors using our approach, binary keys can be generated on demand without user intervention. Through an extensive analysis on our BCS approach using a gait dataset, the results have shown that the binary keys generated using our approach have high entropy for all subjects. The keys can pass both National Institute of Standards and Technology and Dieharder statistical tests with high efficiency. The experimental results also show the robustness of the proposed approach in terms of the similarity of intraclass keys and the discriminability of the interclass keys.

Can a chaos system provide secure communication over insecure networks?—Online automatic teller machine services as a case study

Nowadays, with the proliferation of data exchange across the Internet and the storage of sensitive data on open networks, intensive works are done to secure data. Recently, researchers made more considerable effort to utilize biometric features instead of a memorable password to authenticate the user identity. Based on the theories of chaos map, which are known to possess desirable properties of pseudorandomness, high sensitivity to initial conditions, and very large key space, we propose a biometric cryptosystem for online users’ authentications. The biggest advantage of our proposal is the secure nature of the key. In fact, our system is designed first so that the key never needs to be transmitted or revealed but it will be derived from biometric template and second even if we introduce the same biometric template twice, our system generates different key values. In order to assess the effectiveness of our approach, a biometric-based online automatic teller machine system is proposed and evaluated. Experimental results and statistical analysis, using palmprint as well as palm-vein databases of 200 users, are presented. The obtained results show that the proposed scheme is more secure, fast, and points at increased authentication accuracy. Therefore, it can be implemented in real-time applications, which will undoubtedly help to promote more widespread use of biometric cryptosystems based on chaos theory.

Математические методы в решениях задач третьей международной студенческой олимпиады по криптографии

The mathematical problems and their solutions of the Third International Students' Olympiad in Cryptography NSUCRYPTO'2016 are presented. We consider mathematical problems related to the construction of algebraic immune vectorial Boolean functions and big Fermat numbers, problems about secrete sharing schemes and pseudorandom binary sequences, biometric cryptosystems and the blockchain technology, etc. Two open problems in mathematical cryptography are also discussed and a solution for one of them proposed by a participant during the Olympiad is described. It was the first time in the Olympiad history.

Highly secure palmprint based biometric template using fuzzy vault

SummaryBiometric cryptosystem is an integrated system of cryptography with biometric features which is considered to be a promising component of template security. This paper presents a novel method for biometric cryptosystem based on fuzzy vault using palmprint features. Initially, pre‐processing is performed to remove noise in palmprint enrolled image and to pre‐align image automatically. Bottom‐hat filtering is employed for the feature extraction from the palmprint images. A polynomial is evaluated from the secret key and this polynomial is used to generate projections from palmprint features. Fuzzy vault is created by combining projected points with randomly generated dummy or chaff points. During decoding process, matching scores are calculated by comparing the query feature and stored vault. The vault is successfully decoded when the matched scores should be greater than a predefined threshold value. Experiments were conducted on CASIA database. Simulation result demonstrates that the proposed method gives better performance than other methods and improves the robustness against some security attacks.

Securing templates in a face recognition system using Error-Correcting Output Code and chaos theory

In biometric cryptosystems, biometric data is combined with cryptography algorithms to generate secure templates. In these systems, creating protected templates with both high discriminability and high security is a challenging issue. To address this issue, this paper proposes a new face cryptosystem based on binarization transformation, chaos feature permutation and fuzzy commitment scheme. To enhance discriminability, real-valued templates are converted into their binary versions using a new discriminant binarization transformation based on Error-Correcting Output Code. Then, the chaos feature permutation is used to increase the security and privacy of binary templates, and also to protect the fuzzy commitment scheme against cross-matching attacks. The proposed scheme is evaluated on three well-known face databases, i.e. CMU PIE, FEI, and Extended Yale B. Experimental results show that the proposed method improves discriminability, as well as privacy and security of the system, compared to the existing face template protection algorithms.

Biometric Template Security Triggered by Two Dimensional Logistic Sine Map

The distributed computing environment claims high protection of centralized servers. One of the protection mechanism is biometric authentication which provides a reliable solution to control access on servers. The security of user’s data depends on biometric systems, so such biometric templates should be protected from identity theft. Different approaches have been proposed to protect biometric templates, from that chaos based biometric cryptosystem become popular because of its properties such as ergodicity, determinism, high sensitivity on seed values. To enhance the security, this work developed a new two-dimensional Logistic Sine Map (2DLSM) by merging sine map to standard 2D logistic map. The proposed biometric cryptosystem is the sequential arrangement of confusion and diffusion process. Both processes are carried out by utilizing the chaotic stream generated by 2DLSM. Simulation results and analysis proves that the suggested scheme is fast, secure and efficient to implement in real-time applications.

Flexible fast learning neural networks and their application for building highly reliable biometric cryptosystems based on dynamic features

Abstract The paper concludes an overview of the most promising methods of pattern recognition (deep learning, convolutional, evolutionary, progressive, shallow, wide, hybrid artificial neural networks etc.) in regard to the possibility of their use to build highly reliable biometric cryptosystems on the basis of dynamic features. The authors propose a new approach - the development and training of flexible neural networks. For its implementation, the mathematical apparatus is developed that uses elements of various types of artificial neural networks, the probability theory, and mathematical statistics. The paper presents the results of these studies and formulates the range of the problems to be solved for creating a perspective fundamentals for building highly reliable biometric cryptosystems

Biometric template security: an overview

PurposeThe security of the stored biometric template is itself a challenge. Feature transformation techniques and biometric cryptosystems are used to address the concerns and improve the general acceptance of biometrics. The purpose of this paper is to provide an overview of different techniques and processes for securing the biometric templates. Furthermore, the paper explores current research trends in this area.Design/methodology/approachIn this paper, the authors provide an overview and survey of different features transformation techniques and biometric cryptosystems.FindingsFeature transformation techniques and biometric cryptosystems provide reliable biometric security at a high level. There are many techniques that provide provable security with practical viable recognition rates. However, there remain several issues and challenges that are being faced during the deployment of these technologies.Originality/valueThis paper provides an overview of currently used techniques for securing biometric templates and also outlines the related issues and challenges.

Cancelable and hybrid biometric cryptosystems: current directions and open research issues

Cancelable and hybrid biometric cryptosystems are two techniques used to offer protection against the security and privacy challenges faced by users of biometric authentication systems. The main objective of this paper is to present a critical review of current and emerging trends as well as open research issues in cancellable and hybrid biometric systems. The study examines cancelable biometrics under two main categories, namely non-invertible transformation and biometric salting. It also explores hybrid cryptosystems as means of providing improved template security and user privacy. The review focusses on the modes of operation, performance accuracy, security and privacy of various types of cancellable and hybrid biometric cryptosystems. It also provides a more comprehensive survey of latest research works in cancellable and hybrid biometric cryptosystems than existing review papers in these fields. The paper will provide readers with up-to-date information on current directions and open research issues in cancelable and hybrid biometric cryptosystems.