Fingerprint authentication is extensively adopted due to its ease of capture, low cost sensors and high recognition accuracy. The Minutia Cylinder Code (MCC) is a high-quality feature representation widely used in fingerprint authentication. However, there are two main limitations in the direct use of MCC: redundancy in the feature representation due to overlap between minutiae vicinities, which can lead to inefficient resource utilization; and vulnerability to template inversion attacks, which may expose the original fingerprint data and threaten user privacy. In this paper, we propose a lightweight privacy-preserving fingerprint authentication system that overcomes these limitations through two novel algorithms. The first algorithm, P-MCC, uses the Pearson correlation coefficient to prune MCC features to effectively reduce redundancy and improve resource utilisation, yielding a lightweight design. The second algorithm, S-MCC, applies a secure Boolean function which transforms the pruned MCC features non-invertibly to ensure privacy, thus preventing the reconstruction of original fingerprint data. Together, P-MCC and S-MCC provide a lightweight privacy-preserving fingerprint authentication system, which is well suited to resource-constrained environments, such as the Internet of Things (IoT). Experimental results demonstrate the effectiveness of the proposed system and its practicality in IoT applications.

Face recognition offers seamless human-machine interaction and efficiency. However, its widespread adoption has heightened security and privacy concerns due to the risks associated with compromised biometric data, such as spoofing and unauthorized tracking. To mitigate these concerns, this paper introduces a novel privacy-preserving face recognition framework that integrates an enhanced dual-path feature compression approach with homomorphic encryption (HE) for secure and efficient authentication. We leverage the robust deep neural network model FaceNet to extract discriminative 512-dimensional feature vectors and propose two significantly improved complementary feature compression methods tailored specifically for encrypted biometric systems: (1) Partitioned Principal Component Analysis (P-PCA), which employs a novel segment-wise PCA transformation, preserving localized discriminative information and supporting revocable biometric templates; and (2) Segment-wise Locality-Sensitive Hashing (S-LSH), introducing segment-specific hashing optimized for efficient binary representation and privacy-preserving encrypted-domain computations. Both compressed real-valued and binary features are securely encrypted using HE, enabling direct encrypted-domain similarity computations without exposing sensitive biometric data. Extensive experiments demonstrate that our method achieves competitive authentication performance while maintaining computational efficiency and practical feasibility.

Single-image super-resolution (SISR) aims to reconstruct a high-resolution image from a single low-resolution (LR) image. Although convolutional neural network (CNN)-based SISR methods greatly enhance image restoration, they face critical challenges. First, SISR models using CNNs process image patches uniformly regardless of importance, causing spatial inefficiency in computation and representation. However, due to resource constraints, edge devices in the Internet of Things (IoT) cannot bear heavy computations or large memory storage. Second, most of the existing SISR methods are designed only for the widely adopted bicubic degradation and cannot handle LR images with arbitrary blur kernels, resulting in poor recovery performance. To address these issues, in this article, we propose a two-stage semantic and spatial deep super-resolution (SSDSR) model suitable for the IoT environment. The proposed SSDSR model is capable of handling a variety of blur kernels (e.g., isotropic Gaussian, motion, and disk blur) by effectively using their prior information. Moreover, the semantic feature extraction (SFE) module enables the proposed model to focus on key areas of LR images rather than treating all pixels equally, which significantly reduces the computational load. The semantic information from the SFE module and the spatial information from the spatial attention module are fused adaptively, allowing the proposed model to extract key information in LR images, thereby increasing the representation capacity of the CNN and improving image recovery. Compared with state-of-the-art SISR methods on benchmark datasets, the proposed SSDSR model demonstrates superior performance. When run in real time on an IoT edge device, our model exhibits high computational efficiency and excellent image quality.

Fingerprint authentication techniques have been employed in various Internet of Things (IoT) applications for access control to protect private data, but raw fingerprint template leakage in unprotected IoT applications may render the authentication system insecure. Cancelable fingerprint templates can effectively prevent privacy breaches and provide strong protection to the original templates. However, to suit resource-constrained IoT devices, oversimplified templates would compromise authentication performance significantly. In addition, the length of existing cancelable fingerprint templates is usually fixed, making them difficult to be deployed in various memory-limited IoT devices. To address these issues, we propose a novel length-flexible lightweight cancelable fingerprint template for privacy-preserving authentication systems in various resource-constrained IoT applications. The proposed cancelable template design primarily consists of two components: 1) length-flexible partial-cancelable feature generation based on the designed reindexing scheme and 2) lightweight cancelable feature generation based on the designed encoding nested difference XOR scheme. Comprehensive experimental results on public databases FVC2002 DB1–DB4 and FVC2004 DB1–DB4 demonstrate that the proposed cancelable fingerprint template achieves equivalent authentication performance to state-of-the-art methods in IoT environments, but our design substantially reduces template storage space and computational cost. More importantly, the proposed length-flexible lightweight cancelable template is suitable for a variety of commercial smart cards (e.g., C5-M.O.S.T. Card Contact Microprocessor Smart Cards CLXSU064KC5). To the best of our knowledge, the proposed method is the first length-flexible lightweight, high-performing cancelable fingerprint template design for resource-constrained IoT applications.

Recent findings confirm that biometric templates derived from electroencephalography (EEG) signals contain sensitive information about registered users, such as age, gender, cognitive ability, mental status and health information. Existing privacy-preserving methods such as hash function and fuzzy commitment are not cancelable, where raw biometric features are vulnerable to hill-climbing attacks. To address this issue, we propose the PolyCosGraph, a system based onPolynomial transformation embeddingCosine functions withGraphfeatures of EEG signals, which is a privacy-preserving and cancelable template design that protects EEG features and system security against multiple attacks. In addition, a template corrupting process is designed to further enhance the security of the system, and a corresponding matching algorithm is developed. Even when the transformed template is compromised, attackers cannot retrieve raw EEG features and the compromised template can be revoked. The proposed system achieves the authentication performance of 1.49% EER with a resting state protocol, 0.68% EER with a motor imagery task, and 0.46% EER under a watching movie condition, which is equivalent to that in the non-encrypted domain. Security analysis demonstrates that our system is resistant to attacks via record multiplicity, preimage attacks, hill-climbing attacks, second attacks and brute force attacks.

Authentication plays an essential role in securing the communication between sensor nodes within a wireless body sensor network (WBSN). The electrocardiogram (ECG) as a type of physiological data collected by sensor nodes in WBSNs can provide intrinsic liveness detection and the ECG data are continuously available. These are highly desirable properties for authentication purposes. Although ECG-based intranode authentication for WBSNs has been extensively studied, far less attention is paid for protecting the ECG data despite their sensitivity. In this article, we propose a privacy-preserving ECG-based authentication system using a noninvertible transformation scheme called manipulatable Haar transform (MHT). The proposed authentication system not only provides secure intranode authentication for WBSNs but also protects the sensitive health and identity information contained in ECG data from being exposed to adversaries. The experiment results on two public databases and a real Internet of Things device show the strong performance and efficiency of the proposed system. Moreover, security analysis demonstrates the validity of the MHT.

Identity authentication has become an essential component for access control in the Internet of Things (IoT) environment. To overcome the inherent weakness of password-based authentication, many present IoT devices (e.g., commercial banking smart cards) are equipped with the fingerprint authentication mechanism. However, due to the resource constraints of IoT devices, oversimplified authentication schemes are deployed, which compromise system performance significantly. Moreover, fingerprint templates in these existing schemes are unprotected. To address these issues, we propose an IoT-oriented privacy-preserving fingerprint authentication system. The proposed system is composed of four main components: 1) minutiae extraction; 2) the minutia cylinder-code (MCC)-based cancelable binary template, generated by the proposed normalized random projection; 3) the lightweight, privacy-preserving template, built by novel pairwise Boolean operations; and 4) fingerprint matching. Our system can effectively mitigate preimage and hill-climbing attacks. A prototype of the proposed system is developed using a popular open-source platform (i.e., Open Virtual Platforms). Comprehensive experimental results on eight benchmark data sets validate the effectiveness of the proposed IoT-oriented fingerprint authentication system. Our system also achieves equivalent authentication accuracy to that of the unprotected fingerprint authentication systems deployed in the resource-rich, non-IoT environment. More importantly, our system prototype is deployable to commercially available low-cost smart cards, such as Atmel AT24C256C Memory Smart Card 256K Bits. To the best of our knowledge, the proposed system is the first privacy-preserving, cancelable fingerprint authentication system developed in such a resource-constrained IoT setting.

As a promising candidate to complement traditional biometric modalities, brain biometrics using electroencephalography (EEG) data has received a widespread attention in recent years. However, compared with existing biometrics such as fingerprints and face recognition, research on EEG biometrics is still in its infant stage. Most of the studies focus on either designing signal elicitation protocols from the perspective of neuroscience or developing feature extraction and classification algorithms from the viewpoint of machine learning. These studies have laid the ground for the feasibility of using EEG as a biometric verification modality, but they have also raised security and privacy concerns as EEG data contains sensitive information. Existing research has used hash functions and cryptographic schemes to protect EEG data, but they do not provide functions for revoking compromised templates as in cancellable template design. This paper proposes the first cancellable EEG template design for privacy-preserving EEG-based verification systems, which can protect raw EEG signals containing sensitive privacy information (e.g., identity, health and cognitive status). A novel cancellable EEG template is developed based on EEG features extracted by a deep learning model and a non-invertible transform. The proposed transformation provides cancellable templates, while taking advantage of EEG elicitation protocol fusion to enhance biometric performance. The proposed verification system offers superior performance than the state-of-the-art, while protecting raw EEG data. Furthermore, we analyze the system’s capacity for resisting multiple attacks, and discuss some overlooked but critical issues and possible pitfalls involving hill-climbing attacks, second attacks, and classification-based verification systems.

The significant and rapid development of the Internet of Things (IoT) in recent years has greatly benefited people's lives. However, there are serious security and privacy concerns that need to be addressed when using the IoT for distributed authentication. In this article, we propose a secure fingerprint authentication system to protect user privacy for authentication on IoT devices. The proposed system applies a two-stage feature transformation scheme. Specifically, a weight-based fusion mechanism is designed in the first stage, while the second stage is featured by a linear convolution-based transformation with element removal from the convolution output to increase the security and protection. The proposed authentication system satisfies all the requirements of cancelable biometrics: accuracy, revocability, and diversity, unlinkability, and noninvertibility. Evaluated over six public fingerprint databases, the proposed authentication system exhibits highly competitive performance when compared with the existing cancelable fingerprint templates. Moreover, its energy efficiency on savings in memory space and low computational costs make the proposed scheme a good fit for resource-limited IoT devices.

Searching the identity of an unknown fingerprint over large databases is very challenging. Minutia Cylinder-Code (MCC) has been proved to be very effective in mapping a minutiae-based representation (positions and directions only) into a set of fixed-length transformation-invariant binary vectors. Based on MCC, a Locality-Sensitive Hashing (LSH) scheme has been designed to index fingerprint in large databases, which uses a numerical approximation for the similarity between MCC vectors. However, the LSH scheme is not robust enough when there is certain distortion between template and searched samples, such as fingerprints captured by multi-sensors. In this paper, we propose a finer hash bit selection method based on LSH. Besides, we take into consideration another feature - the single maximum collision for indexing and fuse the candidate lists produced by both indexing methods to produce the final candidate list. Experimentations carried out on our collected multi-sensor database (2D and 3D databases) show that the proposed indexing approach greatly improves the performance of fingerprint indexing. Extensive evaluation was also conducted on some public benchmark databases for fingerprint indexing, and the results demonstrated that the new approach outperforms existing ones in almost all the cases.

With built-in artificial intelligence (AI), edge devices, e.g., smart cameras, can perform tasks like detecting and tracking individuals, which is referred to as edge biometrics. As a driving force for AI, machine/deep learning plays a critical role in edge biometrics. Machine/deep learning based edge biometric systems outperform their nonmachine learning counterpart. However, research shows that artificial neural networks, e.g., convolutional neural networks, are invertible such that adversaries can obtain a certain amount of information about the original inputs/templates. This information leakage is not tolerable for biometric systems because biometric data in the original (raw) templates cannot be reset or replaced. Once compromised, they are lost forever. Therefore, how to prevent original biometric templates from being attacked through inverting deep neural networks is a pressing, but unsolved issue, for deep learning based biometric recognition. To address the issue, in this paper, we develop a novel biometric template protection algorithm using the binary decision diagram (BDD) for deep learning based finger-vein biometric systems. The proposed algorithm is capable of creating a new noninvertible version of the original finger-vein template, which is stacked with an artificial neural network-the multilayer extreme learning machine (ML-ELM) to generate a privacy-preserving finger-vein recognition system, named BDD-ML-ELM. The proposed BDD-ML-ELM ensures the safety of the original finger-vein template even if its transformed version is compromised. The transformed template, if compromised, can be revoked and replaced with another new version by simply changing the user-specific keys. Therefore, the BDD-ML-ELM has a clear advantage over the existing machine/deep learning based biometric systems, whose raw biometric templates are vulnerable when the artificial neural network suffers an inversion attack.

Cancellable fingerprint template has increasingly received interest in research thanks to not only the security for the user's original features but also the stable performance for the system. In this paper, we propose a new method to design cancellable fingerprint template with local structure by clustering the minutiae using the k Nearest Neighbor (kNN) algorithm. In other words, k minutiae in a fingerprint that are closest to a reference minutia form a local structure. Pairwise features from the reference minutia and each of the member in the cluster are extracted and used for local structure matching. The partial Discrete Fourier Transformation was applied as the non-invertible transformation. This method has been evaluated with four public databases FVC2002 DB1-DB3, and FVC2004 DB2. The Equal Error Rate achieved for each database is 0.2%, 0.04%, 4.78%, and 7.64%, respectively.

Smart mobile devices are playing a more and more important role in our daily life. Cancelable biometrics is a promising mechanism to provide authentication to mobile devices and protect biometric templates by applying a noninvertible transformation to raw biometric data. However, the negative effect of nonlinear distortion will usually degrade the matching performance significantly, which is a nontrivial factor when designing a cancelable template. Moreover, the attacks via record multiplicity (ARM) present a threat to the existing cancelable biometrics, which is still a challenging open issue. To address these problems, in this paper, we propose a new cancelable fingerprint template which can not only mitigate the negative effect of nonlinear distortion by combining multiple feature sets, but also defeat the ARM attack through a proposed feature decorrelation algorithm. Our work is a new contribution to the design of cancelable biometrics with a concrete method against the ARM attack. Experimental results on public databases and security analysis show the validity of the proposed cancelable template.

Summary Existing work on partial fingerprint indexing attempts to make full use of the extracted features from the partial segments, such as singular points, minutiae, orientation field, and ridge count. However, singular points may not exist in partial fingerprints, and none of these features can form a complete set of feature vectors that can be used for matching with those derived from the corresponding full fingerprints for indexing. Our former work on fingerprint orientation model based on two‐dimensional Fourier expansion (FOMFE) coefficients‐based fingerprint indexing and global orientation field reconstruction has demonstrated the possibility of reconstructing a global feature vector for partial fingerprint indexing. In this paper, we design some novel features of minutiae triplets in addition to some commonly used features to constitute the local minutiae triplet features. Experiments carried out on fingerprint verification competition (FVC) 2000 DB2a, FVC 2002 DB1a, and National Institute of Standards and Technology (NIST) SD 14 demonstrate the performance improvement after adding the new features to minutiae triplet feature set. We then propose to combine the reconstructed global feature and local minutiae triplet features to improve the performance of partial fingerprint indexing. Specifically, the minutiae triplet‐based indexing scheme and the FOMFE coefficients‐based indexing scheme are applied separately to generate two candidate lists; then, a fuzzy‐based fusion scheme is designed to generate the final candidate list for matching. Experiments carried out on the public database NIST SD 14 show that the proposed approach can improve the performance that has been achieved by individual partial fingerprint indexing algorithms before fusion. Copyright © 2015 John Wiley & Sons, Ltd.

Although some nice properties of the Delaunay triangle-based structure have been exploited in many fingerprint authentication systems and satisfactory outcomes have been reported, most of these systems operate without template protection. In addition, the feature sets and similarity measures utilized in these systems are not suitable for existing template protection techniques. Moreover, local structural change caused by nonlinear distortion is often not considered adequately in these systems. In this paper, we propose a Delaunay quadrangle-based fingerprint authentication system to deal with nonlinear distortion-induced local structural change that the Delaunay triangle-based structure suffers. Fixed-length and alignment-free feature vectors extracted from Delaunay quadrangles are less sensitive to nonlinear distortion and more discriminative than those from Delaunay triangles and can be applied to existing template protection directly. Furthermore, we propose to construct a unique topology code from each Delaunay quadrangle. Not only can this unique topology code help to carry out accurate local registration under distortion, but it also enhances the security of template data. Experimental results on public databases and security analysis show that the Delaunay quadrangle-based system with topology code can achieve better performance and higher security level than the Delaunay triangle-based system, the Delaunay quadrangle-based system without topology code, and some other similar systems.

ABSTRACT Orthogonal frequency division multiplexing (OFDM) transmission equipped with multiple receive antennas constitutes a single‐input multiple‐output (SIMO) OFDM system. SIMO‐OFDM systems have been widely used in wireless communications. Compared to those approaches using training sequences, blind channel estimation methods for SIMO‐OFDM systems have the advantage of saving bandwidth and improving energy efficiency and system throughput. As far as blind channel identification is concerned, it is known that zero padding (ZP)‐based single‐input single‐output (SISO)‐OFDM systems have desirable features compared to conventional cyclic prefix (CP)‐based SISO‐OFDM systems. However, it is yet unknown whether ZP‐ or CP‐based SIMO‐OFDM systems are favourable for blind channel estimation. To investigate this problem, we first propose a short‐data effective method for blind channel estimation for ZP‐based SIMO‐OFDM systems. Then we analyse a number of issues surrounding blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. The issues brought up in the paper have not been discussed in the existing research. The significance of our investigation is that it provides a deep insight into blind channel estimation for ZP‐ and CP‐based SIMO‐OFDM systems. Copyright © 2011 John Wiley & Sons, Ltd.

Bio-cryptography is a new security technology which combines cryptography with biometrics. Fuzzy extractors are effective in terms of binding a cryptographic key to biometric features. However, most existing fuzzy extractors require fingerprint registration prior to the application of fuzzy extractors, and depend on error-correction codes to rectify the biometric uncertainty. This is not operative in practice due to low matching performance. In this paper, by taking full advantage of a Delaunay triangulation net, e.g. local structural stability, we propose a new registration-free Delaunay triangle-based fuzzy extractor. The new fuzzy extractor not only can mitigate biometric uncertainty but also eliminate the feature pre-alignment process in fingerprint authentication. Experimental results show that the proposed scheme achieves a better performance than those of the those of existing registration-based fuzzy extractor methods.

The orthogonal frequency division multiplexing (OFDM) transmission scheme equipped with multiple receive antennas increases robustness against frequency-selective fading and combats loss in SNR. Based on the multichannel signaling property, this letter develops a frequency-domain blind channel estimator for OFDM systems without cyclic prefix (CP). With high data efficiency and low computational complexity, the proposed algorithm is able to identify the channels using a single received OFDM data block. Numerical simulations show that the proposed method performs satisfactorily with only one or very few received OFDM blocks, as compared to the existing subspace-based methods which require many more data records.

Single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) is an appealing multi- carrier transmission technique for combating frequency-selective fading and increasing signal-to-noise ratio. To retrieve transmitted data correctly, reliable estimation of time-dispersive channels is important. This letter develops an improved cross-relation (CR) based blind SIMO-OFDM channel estimation method in the frequency domain. The significance of the proposed algorithm is twofold. First, it is highly data-efficient in that the SIMO-OFDM channels can be blindly identified using a single received data block. Second, the proposed method only requires an upper bound rather than the exact knowledge of the channel length. Simulation results show that the new method performs favorably compared to the existing CR- and subspace-based methods.

This letter develops a fast Fourier transform (FFT)-based method for estimating the impulse response of FIR single-input multiple-output (SIMO) channels driven by an unknown deterministic signal. The proposed algorithm successfully handles very short data sequences, for which the existing second-order statistics based methods, e.g., the subspace (SS), cross-relation (CR), and shifted correlation (SC) algorithms, are known to suffer performance degradation due to inaccurate statistics. The new method significantly outperforms the SS, CR, and SC methods with short sequences of observation data. This proposed method is computationally efficient for achieving good performance when data sequences are inevitably short, as in certain practical applications.

With a rapidly changing channel in mobile communications, there is a scarcity of data samples, posing a challenge to reliable system identification. To address the problem, this paper presents a low complexity frequency-domain approach to blind single-input multiple-output (SIMO) system identification. The proposed approach is straightforward in concept and takes advantage of the computational power of FFT (fast Fourier transform). As a result, the new method is very efficient and effective for short data sequences, for which second-order statistics (SOS) based subspace methods suffer performance deterioration. Therefore, the proposed approach is a desirable alternative to SOS-based subspace methods to achieve good performance when data sequence is inevitably short in certain practical applications

The objective of this paper is to obtain minimum order input-output equations for a class of linear time-varying digital filters in state equation with constant dimension state vector. It is shown that the minimum order of the input-output equation may not be identical to the dimension of the state vector and there exists a nonunique solution for the minimum order input-output equation.