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In this paper, a Phase Congruency based digital color image watermarking algorithm is proposed which provides a higher degree of robustness against attacks and excellent imperceptibility. Here, Phase Congruency has been used to detect the local feature regions of the host image and then the watermark has been infused into it using a new technique called ‘Adaptive α-β Blending’. An accurate Human Visual System modeling has been incorporated via Lifting Wavelet Transform to take the full advantage of perceptual watermarking. The coefficients of the α-β blending are selected adaptively based on the Phase Congruency feature map of the host image. Furthermore, the watermark is secured with a cryptographic algorithm called Arnold’s Cat Map to prohibit further eavesdropping. From rigorous testing, results indicate that our approach is robust against various geometric, non-geometric and combined attacks while maintaining a sublime imperceptibility.

The development of IoT and fog computing promotes various kinds of authentication mechanisms for IoT devices. However, the centralized architecture of traditional authentication schemes leads to the issues of security, privacy, and reliability. Despite the efforts to introduce blockchain to achieve decentralized authentication in existing schemes. Unfortunately, they cannot be directly used under the fog-based IoT environment which consists of resource-constrained IoT devices. To mitigate these issues, we present a blockchain-enabled reliable, and privacy-preserving authentication for fog-based IoT devices, named BPAF. BPAF achieves reliable authentication of fog nodes without violating the privacy of authenticated users during the authentication process. Security analysis and experimental evaluations show that BPAF achieves privacy-preserving and reliable authentication with high efficiency for both the fog nodes and full nodes participating in the authentication process.

Image watermarking technique aims at high imperceptibility of the embedded watermark with minimal distortion in the watermarked images. In frequency-domain, block-based Discrete Cosine Transform (DCT) is a popular method which can be improved by different scaling factors. This paper presents an adaptive scaling factor for selected DCT coefficients in image watermarking. The image blocks that have lowest pixel variances are chosen as the embedding locations. The optimal scaling factors for selected DCT coefficients on the middle frequencies are obtained by finding the best quality of images. The embedding process is performed using the obtained scaling factors. The proposed technique was investigated to verify the robustness and imperceptibility against various image-processing attacks. It is proved that our technique achieves higher robustness against noise addition, filter and compression than the existing schemes in most cases. Our technique produces greater imperceptibility than the existing schemes.

Internet of Things (IoT) devices generate a high volume of data that is processed and stored in traditional cloud computing. The processing including data replication in traditional cloud computing often results in excessive resource utilization, performance overhead, and long response time. Fog computing has been proposed to overcome the shortcomings of cloud computing. Fog computing alleviates the processing and storage burden during data replication from the cloud to the network edge closer to sensor devices. Numerous data replica schemes in fog computing have been proposed to improve the performance efficiency of the data, reduce the turnaround delays of data access, and minimize network latency. However, these schemes do not consider data replication privacy, which is essential for data protection, reliability, and authentication. Therefore, this paper proposes a data replica creation scheme and a data replica placement scheme for preserving the privacy of data in fog computing. Our proposed replica creation scheme is based on a Level of Privacy (LoP) defined by data owners and the service capacity of fog nodes. Our proposed replica placement scheme is based on the priority level of fog nodes. We have conducted a comprehensive experimental analysis to compare the performance of our scheme and the existing schemes. Our results demonstrate that the proposed scheme can significantly achieve efficient replicas privacy, prediction accuracy, as well as outperform the existing state-of-the-art schemes in terms of computational and memory costs.

With the rapid increase in the usage areas of Internet of Things (IoT) devices, it brings challenges such as security and privacy. One way to ensure these in IoT-based systems is user authentication. Until today, user authentication is provided by traditional methods such as pin and token based. But traditional methods have challenges such as forgotten, stolen, and shared with another user who is unauthorized. To address these challenges, we proposed a biometric method called BioSec to provide authentication in IoT integrated with edge consumer electronics using fingerprint authentication. Further, we ensured the security of biometric data both in the transmission channel and database with the standard encryption method. BioSec ensures secure and private communication among edge devices in IoT and Industry 4.0. Finally, we have compared three encryption methods used to protect biometric templates in terms of processing times and identified that AES-128-bit key encryption method outperforms others.

With selective auditory attention (SAA), the human brain can cope with dynamic natural environments efficiently by prioritizing relevant auditory information while filtering out the rest. Auditory saliency describes how bottom-up auditory attention is attracted by local acoustic properties and plays an important role in SAA. In existing neuroimaging studies, the neural processing of auditory saliency has usually been explored via strictly controlled laboratory paradigms with abstract stimuli, overlooking the intrinsic functional interactions among spatially distributed brain networks underlying auditory saliency. In addition, it is unclear how auditory saliency related neural responses vary in auditory information in different semantic categories. In this study, we explored functional brain networks underlying auditory saliency via a multivariate brain decoding approach, in which naturalistic auditory excerpts in three semantic categories (pop music, classic music and speech) were used as stimuli in functional magnetic resonance imaging (fMRI). Our experimental results demonstrate that: 1) Dynamic auditory saliency can be decoded with relatively high accuracy from fMRI brain activities; 2) The neural processing of auditory saliency in different types of auditory information on the one hand shares some common brain regions (e.g., the primary auditory cortex), on the other hand recruits distinct brain regions/networks, e.g., the sensorimotor network and working memory network for classic music and the auditory-language network for speech. Our study provides complementary evidence to the neural processing of auditory saliency under naturalistic experience.

Due to the ever-growing use of active Internet devices, the Internet has achieved good popularity at present. The smart devices could connect to the Internet and communicate together that shape the Internet of Things (IoT). Such smart devices are generating data and are connecting to each other through edge-cloud infrastructure. Authentication of the IoT devices plays a critical role in the success of the integration of IoT, edge, and cloud computing technologies. The complexity and attack resistance of the authentication protocols are still the main challenges. Motivated by this, this paper introduces a lightweight authentication protocol for IoT devices named Light-Edge using a three-layer scheme, including IoT device layer, trust center at the edge layer, and cloud service providers. The results show the superiority of the proposed protocol against other approaches in terms of attack resistance, communication cost, and time cost.

Applications of biometrics in various societal contexts have been increasing in the United States, and policy debates about potential restrictions and expansions for specific biometrics (such as facial recognition and DNA identification) have been intensifying. Empirical data about public perspectives on different types of biometrics can inform these debates. We surveyed 4048 adults to explore perspectives regarding experience and comfort with six types of biometrics; comfort providing biometrics in distinct scenarios; trust in social actors to use two types of biometrics (facial images and DNA) responsibly; acceptability of facial images in eight scenarios; and perceived effectiveness of facial images for five tasks. Respondents were generally comfortable with biometrics. Trust in social actors to use biometrics responsibly appeared to be context specific rather than dependent on biometric type. Contrary to expectations given mounting attention to dataveillance concerns, we did not find sociodemographic factors to influence perspectives on biometrics in obvious ways. These findings underscore a need for qualitative approaches to understand the contextual factors that trigger strong opinions of comfort with and acceptability of biometrics in different settings, by different actors, and for different purposes and to identify the informational needs relevant to the development of appropriate policies and oversight. New journal. Article is available through the open-access option of this journal! ispartof: IEEE Transactions on Technology and Society vol:3 issue:1 pages:1-6 ispartof: location:United States status: Published online

Data integrity verification mechanisms play an important role in cloud environments. Recently, a lightweight identity-based cloud storage audit scheme has been proposed; this paper points out security vulnerabilities of their OffTagGen algorithm. That is, the attackers such as malicious cloud servers can forge the tags, which can destroy data integrity. By improving the construction of OffTagGen algorithm, an improved security cloud auditing protocol is proposed in this work to better protect user’s privacy. The analysis shows that the new protocol is effective and resistant to attacks.

The electronic reporting system can alleviate the problems in terms of efficiency, content confidentiality, and reporter privacy imposed in the traditional reporting system. Relying on anonymity, the privacy of reporters can be protected, but the authentication of reporters with fake names should also be maintained. If authenticated anonymity is guaranteed, the reporters may still conduct misbehaviors such as submitting fake reports after the authentication. To address the above dilemma, we propose to apply a proxy signature to achieve authenticated anonymity and employ blockchain to maintain anonymity yet guarantee traceability for reporters’ misbehaviors. We also propose a new proxy signature scheme in this paper by module lattice for postquantum security. The extensive analysis justified our proposed scheme is secure and manageable.

Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g. cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

Ontology resources are more important for organizing knowledge. It has become an important task to improve the efficiency of ontology construction. How to generate ontology automatically from database is an emerging task in ontology buliding. In this paper we present a reengineering process that transforms relational databases to ontologies. Our goal is to have a well representation of the domain semantics to create a more complete Ontology

Infrastructure as a Service (IaaS) is a kind of cloud sharing service allocated to different category of application at the same time. The shared cloud services are provided through internet for computing, networking, and data storage applications. The utilization of IaaS reduces the maintenance and installation cost of physical hardware modules at the base station. The cloud service providers configure their available cloud components with respect to the suitability of the user requirements. The security protocol available in the IaaS servers are usually better than the traditional local servers. However, the IaaS servers are also open to attacks when the modules encounter misconfiguration and vulnerabilities. The work enforces an encrypted blockchain model for enhancing the quality of service in the IaaS systems on handling image data.

Text and pattern passwords are vulnerable to different attacks like shoulder surfing attacks, guessing attacks, and smudge attacks. To resist these attacks, this article proposes an OP-Grid technique; an implicit graphical authentication scheme having different patterns on every Login session. With a one-time Login pointer, the user creates his password pattern. A legitimate user is authenticated not only through the user-created pattern but also the way he performs it using finger velocity and stroke time features. OP-Grid is implemented on android cell phones, gathered samples from 33 volunteers, and the experimental study carried out to evaluate its accuracy and usability.

With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting a two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective.

In the era of big data, anonymity is recognized as an important attribute in privacy-preserving communications. The existing anonymous authentication and routing are applied at higher layers of networks, ignoring physical layer (PHY) also contains privacy-critical information. In this paper, we introduce the concept of PHY anonymity, and reveal that the receiver can unmask the sender's identity by only analyzing the PHY information, i.e., the signaling patterns and the characteristics of channel. We investigate two practical scenarios, where the receiver has more antennas than the sender in the strong receiver case, and vice versa in the strong sender case. For each scenario, we first investigate sender detection strategy at the receiver, and then we develop corresponding anonymous precoding to address anonymity while guaranteeing high receive signal-to-interference-plus-noise-ratio (SINR) for communications. In particular, an interference suppression anonymous precoder is first proposed, assisted by a dedicated transmit phase equalizer for removing phase ambiguity. Afterwards, a constructive interference anonymous precoder is further investigated to utilize the inter-antenna interference as a beneficial element without loss of sender's anonymity. Simulation demonstrates the proposed anonymous precoders are able to preserve the sender's anonymity and simultaneously guarantee high receive SINR, opening a new dimension on PHY anonymous designs.

MapReduce is a technique used to vastly improve distributed processing of data and can massively speed up computation. Hadoop and its MapReduce relies on JVM and Java which is expensive on memory. High Performance Computing based MapReduce framework could be used that can perform more memory-efficiently and faster than the standard MapReduce. This paper explores an entirely C++ based approach to the MapReduce and its feasibility on multiple factors like developer friendliness, deployment interface, efficiency and scalability. This paper also introduces Delayed Reduction and deployment techniques that can speed up MapReduce in a compiled environment., Comment: 8 pages, 13 figures, 4 authors

Evidence suggests that firms often do not respond to competitive attacks even when they are aware of an attack and have the capability to counter it. We believe that this is because they engage in a form of nonresponse that we designate as strategic forbearance, a phenomenon that has been mostly neglected by scholars of competitive dynamics. We view such forbearance as a critical component of competitive strategy – an attempt to situate responses to attacks within a more complex and nuanced strategic, organizational and environmental context. Forbearance, we argue, represents managers’ mindful attempts to transcend reflexive responses by expanding the range of considerations a) beyond an attacker to other stakeholders and rivals, b) beyond the immediate attack to its historical setting and long-term relational implications, and c) beyond unitary tactics to those concerning global strategic coherence and adaptation. We define formally and tentatively operationalize strategic forbearance, before deriving propositions concerning its five general transcending drivers. Ultimately, we believe, the study of forbearance can bring scholars of competitive dynamics closer to the heart of the reflective competitive strategy.

Due to the characteristics of limited resources and dynamic topology, wireless sensor networks (WSNs) are facing two major problems: security and energy consumption. Nowadays, the trust based solutions are feasible to cope with different bad behaviors of nodes, but there still exist a variety of attacks, high energy consumption and communication congestion between nodes. Therefore, this paper proposes a new trust based secure and energy efficient routing protocol (TBSEER) to solve these problems. TBSEER calculates the comprehensive trust value through adaptive direct trust value, indirect trust value and energy trust value, which can be resistant to black hole, selective forwarding, sinkhole and hello flood attacks. Moreover, the adaptive penalty mechanism and volatilization factor are used to fast identify the malicious nodes. In addition, the nodes only need to calculate the direct trust value, and the indirect trust value is obtained by the Sink, so as to further reduce the energy consumption caused by iterative calculations. Finally, the cluster heads find the safest multi-hop routes based on the comprehensive trust value, which can actively avoid wormhole attack. The simulation results show that the proposed TBSEER reduces network energy consumption, speeds up the identification of malicious nodes, as well as resists all common attacks.

Enhancing security, privacy, and interoperability of biometric networks and protocols has been a challenge for many research works for many years. The several proposed approaches still need to integrate these three characteristics while showing security evidence for biometric applications. Therefore, this paper proposes a probabilistic scheme to encrypt biometric database indexes and a novel approach to interoperability among systems interchanging biometric characteristics, thus enhancing the IEEE Biometric Open Protocol Standard (BOPS). We highlight two meaningful improvements in our research when compared to related works. The first one comes from the proposed cryptographic techniques and network schemes. It implies a negligible probability for known attacks to be successful against the proposal, due to its semantic security evidence, as well as the difficulties that it imposes to the attacks, given the high complexity barriers that are unfeasible for the attacker to break in polynomial time, including the modified initialization vector and the nonce for the encryption algorithm. The second improvement comprises the new integrity and control procedures for biometric identification requests that boost the IEEE BOPS’ reliability and contribute to interoperability purposes. The security analysis, proofs, and results demonstrate that the new proposed biometric network is faultless regarding integrity and interoperability while preserving the anonymity of persons whose biometric data is exchanged in the network and stored in the related databases.

In this paper we present a novel switching function for multiplicative watermarking systems. The switching function is based on the algebraic structure of elliptic curves over finite fields. The resulting function allows for both watermarking generator and remover to define appropriate system parameters, sharing only limited information, namely a private key. Given the definition of the switching function, we prove that the resulting watermarking parameters lead to a stable watermarking scheme., Comment: 8 pages, 3 figures, to be presented at: 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes - SAFEPROCESS

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Two challenging image datasets collected by multiple cameras under various illumination and exposure conditions were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods., Comment: 14 pages, 8 figures, 2 tables, accepted by IEEE Transactions on Image Processing

With the rapid growth of cloud email services, email encryption is beginning to be used more and more to alleviate concerns about cloud privacy and security. However, this increase in usage invites the problem of how to search and filter encrypted emails effectively. Searchable public key encryption is a popular technology to solve encrypted email searching, but encrypted email filtering is still an open problem. We propose an encrypted cloud email searching and filtering scheme based on hidden policy ciphertext-policy attribute-based encryption with keyword search as a new solution. It enables the recipient to search the encrypted cloud email keywords and allows the email filtering server to filter the encrypted email content when receiving the email, as the traditional email keyword filtering service. Our hidden policy scheme is constructed by composite order bilinear groups and proven secure by dual system encryption methodology. Our scheme can be applied to other scenarios such as file searching and filtering and has certain practical value.

In a post-COVID-19 world, extensive study of deep learning-based biometric authentication techniques prompts the need to secure them. Further, the biometric data is assumed to be largely immutable; thus, if it is compromised, it is lost forever. Hence, reliable and secure biometric authentication is of utmost importance. In this paper, we address the security and privacy concerns of biometric templates generated via deep networks. We propose a cancelable biometric authentication approach. The framework consists of a lightweight Convolutional Neural Network (CNN) with a few-shot enrollment for generating biometric templates. Further, to enhance biometric templates’ discriminative power and to provide revocability, biometric templates are projected onto a random subspace (based on the user-specific key). Later projected biometric templates are mapped onto robust n - bit unique codes (using a KNN classifier) and protected via. SHA-3 hash digest. Moreover, a real-world biometric authentication system is always dynamic (users keep on changing). Thus we have also integrated phase-wise incremental learning within a deep learning-based cancelable biometric authentication framework. This is the first work in which deep cancelable templates are generated incrementally to the best of our knowledge. We analyze the proposed scheme for its performance and privacy preservation on three benchmarks constrained iris data-sets and over one unconstrained iris data-set along with one publicly available knuckle data-set. Furthermore, it has been demonstrated that the proposed cancelable incremental framework strictly follows the four fundamental properties of cancelability viz. non-invertibility, unlinkability, revocability, and usability.

To protect the medical images integrity, digital watermark is embedded into the medical images. A non-blind medical image watermarking scheme based on hybrid transform is propounded. In this paper, fingerprint of the patient is used as watermark for better authentication, identifying the original medical image and privacy of the patients. In this scheme, lifting wavelet transform (LWT) and discrete wavelet transform (DWT) are utilized for amplifying the watermarking algorithm. The scaling and embedding factors are calculated adaptively with the help of Local Binary Pattern values of the host medical image to achieve better imperceptibility and robustness for medical images and fingerprint watermark, respectively. Two-level decomposition is done where for the first level LWT is utilized and for the second level decomposition DWT is utilized. At the extraction side, non-blind recovery of fingerprint watermark is performed which is similar to the embedding process. The propounded design is implemented on various medical images like Chest X-ray, CT scan and so on. The propounded design provides better imperceptibility and robustness with the combination of LWT-DWT. The result analysis proves that the proposed fingerprint watermarking scheme has attained best results in terms of robustness and authentication with different medical image attacks. Peak Signal to Noise Ratio and Normalized Correlation Coefficient metrics are used for evaluating the proposed scheme. Furthermore, superior results are obtained when compared to related medical image watermarking schemes.

In order to assess longitudinal effects in fingerprint biometrics several studies have been carried out in the last decade. Almost all of these investigations focused on non-public forensic fingerprint datasets because there is hardly any publicly available fingerprint database allowing experiments and a corresponding analysis of longitudinal aspects is often infeasible as the contained fingerprint samples are either captured in one session only or with a short time interval of only a few weeks between the sessions. With this work a new fingerprint dataset (108,106 samples) is introduced which can be used for inter-session (longitudinal) as well as inter-sensor fingerprint investigations. A total of ten different capturing devices based on distinct sensing technologies (optical, capacative, thermal, multispectral) is utilized and the imprints of all ten fingers of each of the 50 participating volunteers were acquired at four different time-separated sessions over 2 years. This enables inter-session as well as cross-device evaluations. Additionally, a first quality and recognition performance evaluation deploying several state-of-the-art minutiae based fingerprint recognition schemes and NFIQ 2.0 as quality measure is presented.

Data deduplication technique has been widely adopted by commercial cloud storage providers, which is important in coping with the explosive growth of data. To further protect the security of users' sensitive data in the outsourced storage mode, many secure data deduplication schemes have been designed and applied in various scenarios. Among these schemes, secure and efficient re-encryption for encrypted data deduplication attracted the attention of many scholars, and many solutions have been designed to support dynamic ownership management. In this paper, we focus on the re-encryption deduplication storage system and show that the recently designed lightweight rekeying-aware encrypted deduplication scheme is vulnerable to the stub-reserved attack. Furthermore, we propose a secure data deduplication scheme with efficient re-encryption based on the convergent all-or-nothing-transform (CAONT) and randomly sampled bits from the Bloom filter. Due to the property of hash function, our scheme can resist the stub-reserved attack and guarantee the data privacy of data owners' sensitive data. Moreover, instead of re-encrypting the entire package, data owners are only required to re-encrypt a small part of it through the CAONT, thereby effectively reducing the computation overhead. Finally, security analysis and experimental results show that our scheme is secure and efficient in re-encryption.

This paper presents a feature descriptor using Histogram of Oriented Mosaic Gradient (HOMG) that extracts spatial-spectral features directly from mosaic spectral images. Spectral imaging utilizes unique spectral signatures to distinguish objects of interest in the scene more discriminatively. Snapshot spectral cameras equipped with spectral filter arrays (SFAs) capture spectral videos in real time, making it possible to detect/track fast moving targets based on spectral imaging. How to effectively extract the spatial-spectral feature directly from the mosaic spectral images acquired by snapshot spectral cameras is a core issue for detection/tracking. So far, there is a lack of comprehensive and in-depth research on this issue. To this end, this paper proposed a new spatial-spectral feature extractor for mosaic spectral images. The proposed scheme finds two forms of SFA neighborhood (SFAN) to construct a feature extractor suitable for any SFA structure. Exploiting the spatial-spectral correlation in two SFANs, we design six mosaic spatial-spectral gradient operators to compute spatial-spectral gradient maps (SGMs). HOMG descriptors are constructed using the magnitude and orientation of SGMs. The effectiveness and generalizability of the proposed method have been verified with object tracking experiments. Compared to the state-of-the-art feature descriptors, HOMG ranked first on two datasets captured with snapshot spectral camera with different SFAs, achieving a gain of 3.9% and 5.9% in average success rate over the second-ranked feature.

Biometric authentication is, over time, becoming an indispensable complementary component to traditional authentication methods that use passwords and tokens. As a result, the research interest in the protection techniques for the biometric template has also grown considerably. In this paper, we present a light-weight AI-based biometric authentication that operates based on the binary representation of a biometric instance. In details, a binary classifier will be trained using the binary strings that represent the intraclass and interclass biometric subjects. The Support Vector Machine and Multi-layer Perceptron Neural Network are chosen as the classifier to evaluate the fingerprint-based and iris-based authentication capability. Afterward, the authenticated biometric string is fed to a hash function to produce a hash value, which is to be used in a Zero-Knowledge-Proof Protocol for the purpose of privacy preservation. In order to improve the recognition of the classifier, we devise a simple yet efficient strategy to enhance the discriminativeness of the binary strings and name it the Composite Features Retrieval. We evaluated the proposed method with the four publicly available fingerprint datasets FVC2002-DB1, FVC2002-DB2, FVC2002-DB3, and FVC2004-DB2 and the iris dataset UBIRISv1. The promising performance shows this method's capability.

Refereed/Peer-reviewed Video watermarking techniques can be used to prevent unauthorized users from illegally distributing videos across (social) media networks. However, current watermarking solutions are unable to embed a perceptually invisible watermark which is robust to the distortions introduced by camcording. These watermark-disrupting distortions include lossy compression, the addition of noise, frame-rate conversion and geometric distortions. In this paper, we present a novel video watermarking technique that is blind and robust to camcording attacks. The proposed approach uses the integration of the dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) to achieve robustness against geometric attacks. The experimental results validate our technique’s superior imperceptibility and robustness to several attacks when compared to existing peer mechanisms. In conclusion, the proposed technique can be used to protect against illegal distribution of video content.

With the convenient availability of remote sensing data, how to make models to interpret complex remote sensing data attracts wide attention. In remote sensing data, hyperspectral images contain spectral information and LiDAR contains elevation information. Hence, more explorations are warranted to better fuse the features of different source data. In this paper, we introduce semantic understanding to dynamically fuse data from two different sources, extract features of HSI and LiDAR through different capsule network branches and improve self-supervised loss and random rigid rotation in Canonical Capsule to a high-dimensional situation. Canonical Capsule computes the capsule decomposition of objects by permutation-equivariant attention and the process is self-supervised by training pairs of randomly rotated objects. After fusing the features of HSI and LiDAR with semantic understanding, the unsupervised extraction of spectral-spatial-elevation fusion features is achieved. With two real-world examples of HSI and LiDAR fused, the experimental results show that the proposed multi-branch high-dimensional canonical capsule algorithm can be effective for semantic understanding of HSI and LiDAR. It indicates that the model can extract HSI and LiDAR data features effectively as opposed to existing models for unsupervised extraction of multi-source RS data., Comment: 10 pages

The privacy paradox suggests that privacy concerns do not relate to privacy-related behavior. Although it has inspired numerous studies, findings remain inconclusive. Some of the inconsistencies in published findings may be explained by a strong heterogeneity in the conceptual and analytical choices that researchers implement when investigating the privacy paradox. Based on representative survey data of the 27 EU member states (2011: n = 8,962; 2015: n = 10,526; 2019: n = 11,428), I investigated the effect of conceptual and analytical decisions on ‘finding’ the privacy paradox. Specification curve analyses revealed that the magnitude and statistical significance of the relationship between privacy concerns and information disclosure is contingent on the operationalization of the independent variable, the inclusion of covariates, and the age of the studied population. The relationship between online privacy concerns and using social media privacy settings, in contrast, was less influenced by analytical decisions. Yet, the relationship was stronger in younger people and increased over time. The findings call for more transparency in analyzing research data. Evaluating the implications of analytical choices will help to establish best practices and advance cumulative knowledge creation in privacy research.

This study explores privacy from the perspective of the user. It leverages a “framing in thought” approach to capture how users make sense of privacy in their social media use. It builds on a unique dataset of privacy definitions collected from a representative sample of 608 US social media users. The data are analyzed using topic modeling and semantic network analysis to unpack the multidimensionality of social media privacy. These dimensions are further examined in relation to established demographic antecedents of privacy concerns and behaviors. Results indicate the dominance of frames related to horizontal dimensions of privacy, or privacy vis-à-vis peers, as compared with the vertical dimensions, or privacy vis-à-vis institutions. In addition, the findings suggest that user conceptualization of privacy reflects a cognate-based approach that emphasizes control and limits to information access. Implications for privacy research, policy, and technology design are discussed.

Trust in financial institutions is widely considered important. However, a clear overview of studies on the drivers of trust is missing. We intend to fill this gap in the literature. After discussing why trust in financial institutions is important, we turn to its measurement, where we distinguish between trust in one's own institution and trust in institutions in general (narrow-scope and broad-scope trust), and discuss how these measures differ from generalized trust (i.e. trust in other people with whom there is no direct relationship). Finally, we survey the determinants of trust in financial institutions and discuss a wide range of drivers. First, trust in financial institutions depends on the economic situation: it behaves procyclically and is negatively affected by financial crises. Second, the behavior of financial institutions matters: prudent conduct, the provision of good services and financial health have a positive effect on trust. Third, although consumer characteristics also relate to trust, many of these relationships are context-dependent. Fourth, there is a positive association between narrow-scope trust on the one hand and broad-scope trust and generalized trust on the other. Last, policy measures and supervisory actions can help prevent loss of trust.

With the advancement of the 5G network, the Internet of Vehicles (IoV) is becoming more and more attractive for academic researchers and industrial. A main challenge of IoV is to guarantee the authenticity of messages and protect drivers’ privacy simultaneously. The majority of privacy-preserving authentication schemes for IoV adopt pseudonyms or group signatures to achieve a balance between security and privacy. However, defending the Sybil attacks in these schemes is challenging. In this work, we propose a novel privacy-preserving authentication scheme for announcement messages, which utilizes the trajectories of vehicles as their identities. When an authenticated message is verified, the verifier is convinced that the message is generated by a vehicle that has a unique masked trajectory. Meanwhile, the real trajectories of vehicles are kept private. In particular, our scheme achieves Sybil attack resistance without the limitation of trajectory length even when the attacker is allowed to use cloud services.

Due to the characteristics like limited resources and dynamic topology, wireless sensor networks (WSNs) are facing two major problems such as security and energy consumption. To deal with various improper behaviors of nodes the trust-based solutions are possible but still exist a variety of attacks, high energy consumption, and communication congestion between nodes. Therefore, this paper proposes an advanced and efficient trust-based secure and energy-efficient routing protocol (TBSEER) to solve these network problems and to avoid malicious nodes. Efficient Adaptable Ant Colony Optimization Algorithm (EAACO) calculates the comprehensive trust value through adaptive direct trust value, indirect trust value, and energy trust value, which can be resistant to internal network attacks such as sinkhole, black hole, selective forwarding, and hello flood attacks. In addition, to fast identify the malicious nodes in the WSN, the adaptive penalty mechanism and volatilization factor are used. Moreover, the nodes only need to calculate the direct trust value, and the indirect trust value is obtained by the sink, so as to further reduce the energy consumption caused by iterative calculations. To actively avoid network attacks, the cluster heads find the safest multi-hop routes based on the comprehensive trust value. The simulation results show that the proposed EAACO reduces network energy consumption, speeds up the identification of malicious nodes, as well as resists all common attacks. Keywords: Comprehensive trust value, direct trust value, indirect value, EAACO, network attacks, wireless sensor networks

Cloud computing is actually a model for enabling convenient, limitless, on demand network access to a shared pool of computing resource. This paper describes introductory part explain the concept of cloud computing, different components of cloud, types of cloud service development. At last paper elaborates the classification of cloud computing which will clear the ovelall idea of cloud computing to the learners who are new to this field. Keywords: cloud computing, SaaS, PaaS, IaaS

Smartphones have become a crucial way of storing sensitive information; therefore, the user's privacy needs to be highly secured. This can be accomplished by employing the most reliable and accurate biometric identification system available currently which is, Eye recognition. However, the unimodal eye biometric system is not able to qualify the level of acceptability, speed, and reliability needed. There are other limitations such as constrained authentication in real time applications due to noise in sensed data, spoof attacks, data quality, lack of distinctiveness, restricted amount of freedom, lack of universality and other factors. Therefore, multimodal biometric systems have come into existence in order to increase security as well as to achieve better performance.[1] This paper provides an overview of different multimodal biometric (multibiometric) systems for smartphones being employed till now and also proposes a multimodal biometric system which can possibly overcome the limitations of the current biometric systems. Keywords: Biometrics, Unimodal, Multimodal, Fusion, Multibiometric Systems

In the last decade, 3D models gained interest in many applications, such as games, the medical field, and manufacture. It is necessary to protect these models from unauthorized copying, distribution, and editing. Digital watermarking is the best way to solve this problem. This paper introduces a robust watermarking method by embedding the watermark in the low-frequency domain, then selecting the coarsest level for embedding the watermark based on the strength factor. The invisibility of the watermark for the proposed algorithm is tested by using different measurements, such as HD and PSNR. The robustness was tested by using different types of attacks; the correlation coefficient was applied for the evaluation of the extracted watermark. From the results, it is clear that the proposed algorithm achieved good invisibility and robustness against the geometrical attack and the added noise.

In the current era, it is necessary to device authorization and authentication techniques to secure resources in information technology. There are several methods to substantiate authorization and authentication. User authentication is essential for authenticating user access control in WSNs. Biometric recognition error, lack of anonymity and vulnerability to attacks, user verification problem, revocation problem and disclosure of session key by the gateway node are some of the security flaws encountered. In this study, a Multimodal Authentication Scheme for Wireless Sensor Networks (WSN-MAS) is proposed to authenticate legitimate users. The main objective is the fusion of fingerprint and iris biometric features at feature level to enable additional accuracy to verify and match user identity with stored templates. In this paper, multimodal biometric features are used for authentication to improve performance, reduce system error rates to achieve better security in WSN.

Fingerprint recognition is one of the most well-known biometrics and the most widely utilized biometric option for computerized system authentication. The traditional secret key and token-based verification methods are gradually being phased out in favor of biometric solutions. The two most important aspects to consider when designing a biometric system are security and accuracy of recognition. In this work, a comprehensive assessment is presented to throw light on the most recent advances in the study of unique fingerprint-based biometrics from these two perspectives, with the objective of enhancing system security and recognition accuracy. Also, researchers suggest utilizing a normalized fingerprint model to combine fingerprints from various layouts. Constrictions of previous exploration work are highlighted, and ideas for future work are made, based on an in-depth investigation and discussion. According to the report, scientists continue to have issues dealing with the two most fundamental attacks on biometric systems, namely, attacks on the User Interface (UI) and attacks on format databases. Building genuine remedies to thwart these attacks, while ensuring strong security while maintaining high recognition precision, is a hot research topic right now, and will be in the near future. Furthermore, recognition accuracy in less-than-ideal circumstances is sure to be unsatisfactory, necessitating special attention in the design of a biometric system. This research also depicts related obstacles and patterns of momentum exploration.

In this paper, we propose a novel cryptographic primitive named reusable group fuzzy extractor (RGFE) allowing any member of a group to extract and reproduce random strings from a fuzzy and non-uniform source of high entropy (called fingerprint). Any group member can anonymously generate a random string for the group using his fingerprint and can be traced when needed, whereas other members can reproduce the string using their own fingerprints. Moreover, a fingerprint can be repeatedly used to generate multiple random strings. Basing on RGFE, we present group-shared Bitcoin wallet, which can be used by a group of users to receive or spend coins via biometrics in a traceable way.

With the rapid development of brain-computer interface technology, as a new biometric feature, EEG signal has been widely concerned in recent years. The safety of brain-computer interface and the long-term insecurity of biometric authentication have a new solution. This review analyzes the biometrics of EEG signals, and the latest research is involved in the authentication process. This review mainly introduced the method of EEG-based authentication and systematically introduced EEG-based biometric cryptosystems for authentication for the first time. In cryptography, the key is the core basis of authentication in the cryptographic system, and cryptographic technology can effectively improve the security of biometric authentication and protect biometrics. The revocability of EEG-based biometric cryptosystems is an advantage that traditional biometric authentication does not have. Finally, the existing problems and future development directions of identity authentication technology based on EEG signals are proposed, providing a reference for the related studies.

Biometric Identification is a globally renowned procedure, which has been utilised to achieve a successful and accurate level of identification. In the sea of biometrics, fingerprints are deemed more popular when it comes to verification. This results from the presence of the ridges on the fingerprints that are completely exclusive to each individual. Besides that, fingerprints are expansively employed to ascertain and authenticate people individually. Therefore, this study had proposed to employ distinctive Edge Detection techniques together with the Hough Transform to match the images of the fingerprints in a fingerprint matching system. The Hough Transform is a superior procedure carried out to get an accurate series of finer points or lines. The finer points or lines would then distinguish the fingerprints. Nevertheless, it was still a challenge to extract finer points or lines from the fingerprints under uninhibited conditions. Therefore, this paper was organised based on four distinctive steps. First, different Edge Detection operators were employed to perform the fingerprint matching algorithm. Next, the fingerprint matching algorithm was applied twice to the same Edge Detection operators. Thirdly, the Edge Detection operators had been substituted with the Transformation Method for the same matching procedure. For example, the proposed fingerprint matching algorithm comprised of the Hough Transform and same Edge Detection operators. Finally, distinct Edge Detection operators based on the decision making algorithm were used to calculate and determine the percentage of matching. Therefore, this study proved that the prints obtained via the Prewitt Edge Detection together with Hough Transform were in an agreement.

Over the past years, Cloud computing has become one of the most influential information technologies to combat computing needs because of its unprecedented advantages. In spite of all the social and economic benefits it provides, it has its own fair share of issues. These include privacy, security, virtualization, storage, and trust. The underlying issues of privacy, security, and trust are the major barriers to the adoption of cloud by individuals and organizations as a whole. Trust has been the least looked into since it includes both subjective and objective characteristics. There is a lack of review on trust models in this research domain. This paper focuses on getting insight into the nomenclature of trust, its classifications, trust dimensions and throws an insight into various trust models that exist in the current knowledge stack. Also, various trust evaluation measures are highlighted in this work. We also draw a comparative analysis of various trust evaluation models and metrics to better understand the notion of trust in cloud environments. Furthermore, this work brings into light some of the gaps and areas that need to be tackled toward solving the trust issues in cloud environments so as to provide a trustworthy cloud ecosystem. Lastly, we proposed a Machine Learning backed Rich model based solution for trust verification in Cloud Computing. We proposed an approach for verifying whether the right software is running for the correct services in a trusted manner by analyzing features generated from the output cloud processed data. The proposed scheme can be utilized for verifying the cloud trust in delivering services as expected that can be perceived as an initiative towards trust evaluation in cloud services employing Machine learning techniques. The experimental results prove that the proposed method verifies the service utilized with an accuracy of 99%.

The Internet of things (IoT) generates a plethora of data nowadays, and cloud computing has been introduced as an efficient solution to IoT data management. A cloud resource administrator usually adopts the replication strategy to guarantee the reliability of IoT data. This mechanism can significantly reduce data access time, and evidently, more replicas of data increase the data storage cost. Furthermore, the process of selecting mini clouds for replica allocation and sorting replicas in mini clouds is considered an NP-hard problem. Therefore, this paper proposes an approach based on the harmony search (HS) algorithm to allocate replicas to the IoT data in the cloud computing environment in order to mitigate the data access cost. The HS algorithm was employed in the proposed approach to determine the best location for data replication in the cloud computing environment. According to the implementation results, the proposed approach outperformed the other methods and managed to significantly decrease data access time and delay as well as energy consumption.

Although biometric authentication is viewed as more prominent than password or token-based methodology in identity verification, biometric templates are vulnerable to attacks. This paper proposes a new approach for securing fingerprint templates using elliptical structures generated from the fingerprint minutiae. Authors generate a feature vector from the ellipse and will be projected onto a 3D-space to compute a binary string. The resultant binary string is transformed to frequency domain (DFT) and multiplied with a user specific random matrix to make it permanently non-invertible and secure. The results show the efficacy of the proposed method for protecting the fingerprints.

This paper is based on a keynote lecture presented by Prof. Fechte-Heinen during the 2nd International Conference on Quenching and Distortion Engineering in April 2021. Firstly, it presents a short summary of the basics of distortion formation. The mechanisms and the potential for distortion with its carriers are discussed in more detail. Furthermore, the method of Distortion Engineering is explained. Finally, selected examples are presented that illustrate the connections between distortion and the quenching process.

In the recent times, with an increase in human identity thefts, the fingerprint-based biometric systems play a significant role in secured authentication and access restrictions. The security and privacy are key aspects that need peculiar reflection while designing these recognition systems. However, imposters practice several presentation attack instruments (PAIs) to exploit the biometrical infrastructure to breach the security aspects. Though, biometric systems are receptive to diverse threats, but the presentation attacks (PAs) are the most widely attempted in current scenario. In PA, an attacker makes use of an artifact of a real biometric trait in order to circumvent the sensor module of the system. In this article, we expound state-of-the-art fingerprint presentation attack detection (FinPAD) mechanisms along with taxonomy covering the period of 2001–2021. The article presents a comprehensive survey of classical hardware-based, handcrafted fingerprint PAD approaches with a special focus on recent deep leaning-based techniques. We provide a summary of publically available fingerprint anti-spoofing databases, standard PAD evaluation protocols, and fingerprint liveness detection competition (LivDet) series till the year 2021. The study explored several open research challenges that yield future directions to the investigators in this active field of research. Our study reveals that the modern data-driven FinPAD techniques are robust and efficient as compared to their hardware-based counterpart in terms of performance. However, designing lightweight fingerprint PAD techniques with smaller datasets that offer better performance in cross-dataset, cross-material, and cross-sensor scenario still remain an open research issue.

We proposed architecture for sharing electronic health records. This work is based on an encryption mechanism to encrypt the health data, access control to ensure the privacy and confidentiality of health records. The proposed scheme has used a storage mechanism combining cloud and blockchain. The cloud server stores the encrypted health records, while the blockchain retains traceable log information and encryption keys. The proposal is an adequate solution to share the electronic health record securely. Our solution allows integrity, privacy, and confidentiality to ensure efficient protection of sharing electronic health records.