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In this paper, we define noiseless privacy, as a nonstochastic rival to differential privacy, requiring that the outputs of a mechanism (i.e., function composition of a privacy-preserving mapping and a query) attain only a few values while varying the data of an individual (the logarithm of the number of the distinct values is bounded by the privacy budget). Therefore, the output of the mechanism is not fully informative of the data of the individuals in the dataset. We prove several guarantees for noiselessly-private mechanisms. The information content of the output about the data of an individual, even if an adversary knows all the other entries of the private dataset, is bounded by the privacy budget. The zero-error capacity of memory-less channels using noiselessly private mechanisms for transmission is upper bounded by the privacy budget. The performance of a non-stochastic hypothesis-testing adversary is bounded again by the privacy budget. Assuming that an adversary has access to a stochastic prior on the dataset, we prove that the estimation error of the adversary for individual entries of the dataset is lower bounded by a decreasing function of the privacy budget. In this case, we also show that the maximal leakage is bounded by the privacy budget. In addition to privacy guarantees, we prove that noiselessly-private mechanisms admit composition theorem and post-processing does not weaken their privacy guarantees. We prove that quantization or binning can ensure noiseless privacy if the number of quantization levels is appropriately selected based on the sensitivity of the query and the privacy budget. Finally, we illustrate the privacy merits of noiseless privacy using multiple datasets in energy, transport, and finance.

With the fast increasing of the electronic crimes and their related issues, deploying a reliable user authentication system became a significant task for both of access control and securing user’s private data. Human biometric characteristics such as voice, finger, iris scanning, face, signature and other features provide a dependable security level for both of the personal and the public use. Many biometric authentication systems have been approached for long time. Due to the uniqueness of human biometrics witch played a master role in degrading imposters’ attacks. Such authentication models have overcome other traditional security methods like passwords and PIN. This paper aims to briefly address the psychological biometric authentication techniques and a brief summary to the advantages, disadvantages of each method. Main contribution it found that used EEG signals, as biometrics is the best technique compare to with five other techniques.

The cloud server stores its unique DE-duplicate data replication cloud storage system as outsourced only data copies. Deduplication cloud storage audits can verify the integrity of data stored in the cloud, while the cloud calculates the need to maintain only one copy of the copied file. The existing de-duplication auditing solutions for cloud storage are vulnerable to brute-force dictionary attacks, which is a disclosure leakage of user privacy and more time consumption to find a duplicate files in cloud. Users must check the integrity of lightweight cloud computing data to create data authentication and retrieve files in the cloud. The proposed system uses a secure Array Preserving Triple Data Encryption (AP3DE) algorithm to verify with confidence the expertise of aggregate technology. In this method to analysis the user upload document before store on cloud. The aggregator to provide an authentication access the data access role to cloud user. Cloud protects the privacy, confidentiality, and integrity of data stored in the cloud by generating the code of the authentication file and the encryption key of the file through the proposed system test. The Index-level Boundary Pattern Convergent Encryption (IBPCE) algorithm integrates dynamic data handling and data exclusion capabilities for secure cloud storage privacy security public audits. The cloud data security using a Convergent international data encryption algorithm used to encrypted data. It is a 64 bit plaintext and cipher text blocks is controlled by 128 bit key. Analysis of safety and efficiency shows that the proposed mechanism is highly efficient and safe. In this proposed IBPCE method to manage and reduce the cloud storage compare to other existing method.

In the recent years the utilization of cloud to store and process the big data is increased rapidly. Security is the major concern faced by the big data in cloud environment. This paper proposed the authentication scheme for preserving the security and privacy of the big data in cloud environment. This paper considered the medical big data processing from the telemedicine system as the big data. The medical big data consists of huge volumes of medical records which are stores in the cloud. In the proposed mechanism follows mutual authentication and performs encryption operation between user and cloud environment. The proposed authentication mechanism has less computational load compared to the existing algorithms.

Watermarking images is essential for several reasons like security, validation etc. The extracted watermark should have similar characteristics as that of the watermark image embedded during the watermarking process. Also, it is important that the watermark image should not differ the features of the host image after embedding. In order conceive this features, it is important to have a technique of watermarking. In this paper, such an attempt has been made to incorporate the novel techniques of watermarking using single value decomposition (SVD), discrete wavelet transformation (DWT) and social group optimization algorithm (SGOA). The simulation-based experiment has been carried out to evaluate the performance of the technique in terms of structural similarity index (SSI). The Matlab environment is used for the simulations.

Cybercrime continues to cause increasing threat to business processes, eroding stakeholders’ trust in Internet technologies. In this article, we explore how six dominant algorithmic trust positions facilitate cognitive processing, which, in turn, can influence an organization's productivity and align its values and support structures for combating cybercrimes. This conceptual paper uses a cognitive perspective described as a throughput model. This modeling perspective captures several dominant algorithmic trust positions for organizations, providing a new, and powerful approach which seeks to enhance our understanding of the cognitive representation of decision-making processes. These trust positions are rational-based trust, rule-based trust, category-based trust, third-party based trust, role-based trust, and knowledge-based trust. Finally, we provide conclusion and implications for future research.

Localisation is an important part of many applications. Our motivating scenarios are short-term construction work and emergency rescue. These scenarios also require rapid setup and robustness to environmental conditions additional to localisation accuracy. These requirements preclude the use of many traditional high-performance methods, e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To overcome these challenges, we introduce iMag+, an accurate and rapidly deployable inertial magneto-inductive (MI) mapping and localisation system, which only requires monitored workers to carry a single MI transmitter and an inertial measurement unit in order to localise themselves with minimal setup effort. However, one major challenge is to use distorted and ambiguous MI location estimates for localisation. To solve this challenge, we propose a novel method to use MI devices for sensing environmental distortions for accurate closing inertial loops. We also suggest a robust and efficient first quadrant estimator to sanitise the ambiguous MI estimates. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy and can improve performance significantly compared with only using a Magneto-inductive device or inertial measurement unit (IMU) for localisation.

The rapid advancement of cloud computing has promoted the development of cloud storage services. One of the biggest concerns of cloud users is whether the completeness and recoverability of data can be guaranteed when cloud servers encounter problems. Only when the integrity of data is fully guaranteed can users consume cloud storage with confidence, especially in a complicated cloud environment with multiple clouds. However, the literature still lacks a thorough survey on cloud data integrity auditing for both single copy and multiple replicas. In this article, we survey and compare existing auditing schemes for single copy and multiple replicas based on a set of criteria. Based on our review and analysis, we discuss open issues, potential applications and future directions in the field of the integrity auditing in the cloud, including the implications of such trendy topics as merging blockchain and edge computing into data integrity auditing.

In the era of cloud computing, massive quantities of data are encrypted and uploaded to the cloud to realize a variety of applications and services while protecting user confidentiality. Accordingly, the formulation of methods for efficiently searching encrypted data has become a critical problem. Public-key encryption with keyword search is an efficient solution that allows the data owner to generate encrypted keywords for a given document while also allowing the data user to generate the corresponding trapdoor for searching. Huang and Li proposed a public-key authenticated encryption with keyword search (PAEKS) scheme to resist keyword guessing attacks, where the data owner not only encrypts keywords but also authenticates them. However, existing PAEKS-related schemes carry a tradeoff between efficiency, storage cost, and security. In this article, we introduce a novel framework called identity-certifying authority-aided identity-based searchable encryption, which has the advantage of reducing storage space while remaining the efficiency and security. We formally define the system model and desired security requirements to represent attacks in a real scenario. In addition, we propose a provably secure scheme based on the gap bilinear Diffie–Hellman assumption and experimentally evaluate our scheme in terms of its performance and theoretical features against its state-of-the-art counterparts.

Acoustic fingerprinting aims to identify a mobile device based on its internal microphone(s) and speaker(s) which are unique due to manufacturing imperfection. This paper seeks a thorough understanding of the (in)security of exploring acoustic fingerprints for achieving distributed mobile authentication. Our contributions are threefold. First, we present a new acoustic fingerprint-emulation attack and demonstrate that it is a common vulnerability of acoustic mobile authentication systems. Second, we propose a dynamic challenge-response defense to secure acoustic mobile authentication systems against the acoustic fingerprint-emulation attack. Finally, we thoroughly investigate existing acoustic fingerprinting schemes and identify the best option for accurate, secure, and deployable acoustic mobile authentication systems.

Privacy concerns of using sensitive biometric data as credentials arise with the wide adoption of user-friendly face authentication. To protect the facial features of users, two important functions, i.e. revocability and reusability, are anticipated to be realized in a privacy-preserving face authentication design. Revocability requires an effective approach to deregister or replace user credentials when the authentication server is compromised; For reusability, the same credentials should appear independently t

Digital data sharing is increased day by day due to the rapid access of the Internet by mass people. Multimedia, for example text, image, video, and audio can be easily accessed by unauthorized users. To ensure multimedia security, researchers are focusing on digital image watermarking. A robust watermarking system must satisfy the basic design requirements like imperceptibility, robustness, security, and payload capacity. Watermarking can be embedded into a host image through different transform operations, such as discrete cosine transform (DCT), discrete wavelet transform (DWT), and singular value decomposition (SVD). However, a single transformation does not ensure all the design requirements simultaneously. To fill this gap, a hybrid blind digital image watermarking with a combination of DCT, DWT, and SVD is proposed in this paper. At first, the Arnold map is used to encrypt the watermark image. Then DCT is applied to it and to the host image followed by DWT before SVD. After that, the watermarked image is generated by embedding the watermark image into the host image. The performance is evaluated under various attacks and experimental results show that our algorithm ensures improved robustness while preserving high imperceptibility and enhanced security than the state-of-the-art methods.

Wearable computing has been used in a wide range of applications. But wearable computing often suf-fers from various security and privacy issues. To solve these issues, many effective authentication schemes have been proposed. However, most of the existing schemes are vulnerable to various known attacks (such as desynchro-nization attack, privileged-insider attack, and anonymity attack), or require high computation and communication costs, and are not suitable for resource-constrained wear-able devices, or simultaneous verification of multiple wearable devices is not supported. Therefore, in this paper, we propose a new anonymous authentication and group proof protocol for wearable computing, which achieves mutual authentication between the wearable device and user and between user and cloud server, and generates a group proof for multiple wearable devices. Further, we extend the Real-Or-Random (ROR) model to support an-onymity and group proof, and formally prove that the proposed scheme is provably secure under the extended security model. In addition, the informal security analysis is demonstrated that the proposed scheme is more resilient against known attacks. Finally, compared with some existing schemes, the proposed scheme offers more functionality features and requires less communica-tion and computation costs.

Visible watermarking offers direct copyright protection to the naked eye using visible pattern superimposed in the host image. However, due to their exposure, the watermark pattern becomes an easy target to remove by malicious entities that generates copyright-free images. Until now, in the scientific literature several semi-automatic visible watermark removal attacks have been proposed, showing enough visual quality of the recovered host image. In this paper, we propose a watermark design based on modified Seam Carving technique to provide robustness against removal attacks including inpainting-based attacks, Independent Component Analysis (ICA)-based attacks, and image matting-based attacks. Robustness against the attacks is measured using common image quality assessments and a watermark visibility assessment based on the Human Visual System (HVS). Experimental results show robustness of the proposed method against above mentioned watermark removal attacks under the most severe situation, in which adversary has the logotype used as a watermark pattern because it is public in Internet. The proposed method can be applied to any visible watermarking algorithms to make them robust against illegal removal attempts, without sacrificing their original functionality, such as reversibility for authorized users.

In this paper, we propose centralized and decentralized privacy-preserving and collusion-resistant charging coordination schemes for Energy Storage Units (ESUs). The centralized charging coordination (CCC) scheme is useful where robust communication infrastructure is available that connects ESUs to a charging coordinator (CC), whereas the decentralized charging coordination (DCC) scheme is useful in remote areas and isolated microgrids. In CCC scheme, ESUs acquire tokens to send charging request anonymously to the CC via local aggregators. So, if the CC and the aggregator collude, they cannot identify requests' senders. To prevent linkability attacks, ESUs sends multiple requests with random Time to complete charge (TCC) and state of charge (SoC). Then, the CC compute schedules to maximize the power delivered to ESUs. In DCC scheme, charging is coordinated using a privacy-preserving data aggregation technique. Each ESU selects some ESUs as proxies, and shares a secret mask with each proxy. Then, each ESU adds a mask to its request and encrypts it so by aggregating requests, all masks are nullified and the total demand is known. DCC scheme is secure against collusion attacks due to the masking technique. The results of extensive experiments confirm that our schemes are efficient and secure, and preserve ESUs privacy.

Real-time, accurate, and robust localisation is critical for autonomous vehicles (AVs) to achieve safe, efficient driving, whilst real-time performance is essential for AVs to achieve their current position in time for decision making. To date, no review paper has quantitatively compared the real-time performance between different localisation techniques based on various hardware platforms and programming languages and analysed the relations among localisation methodologies, real-time performance and accuracy. Therefore, this paper discusses state-of-the-art localisation techniques and analyses their localisation performance in AV application. For further analysis, this paper firstly proposes a localisation algorithm operations capability (LAOC)-based equivalent comparison method to compare the relative computational complexity of different localisation techniques; then, it comprehensively discusses the relations among methodologies, computational complexity, and accuracy. Analysis results show that the computational complexity of localisation approaches differs by a maximum of about 〖10〗^7 times, whilst accuracy varies by about 100 times. Vision- and multi-sensor fusion-based localisation techniques have about 2–5 times potential for improving accuracy compared with lidar-based localisation. Lidar- and vision-based localisation can reduce computational complexity by improving image registration method efficiency. Multi-sensor fusionbased localisation can achieve better real-time performance compared with lidar- and vision-based localisation because each standalone sensor does not need to develop a complex algorithm to achieve its best localisation potential. Vehicle-to-everything (V2X) technology can improve positioning robustness. Finally, results show that the fusion technique combined with V2X has considerable potential for achieving a cost-efficient localisation solution for AVs.

Remote user authentication schemes provide a system to verify the legitimacy of remote users’ authentication request over insecure communication channel. In last years, many authentication schemes using password and smart card have been proposed. However, password might be revealed or forgotten and smart card might be shared, lost or stolen. In contrast, the biometrics, such as face, fingerprint or iris, have no such weakness. With the trend of mobile payment, more and more applications of mobile payment use biometrics to replace password and smart card. In this paper, we propose a biometric-based remote authentication scheme substituting biometric and mobile device bounded by user for password and smart card. This scheme is more convenient, suitable and securer than the schemes using smart cards on mobile payment environment.

In this paper, we study a cloud security service market, which is composed of cloud users and cloud security service vendors (CSSVs). The CSSVs work as the insurers for selling the cloud security plan, which is consisted of cloud security service and cloud-insurance. The users in the cloud platform can purchase the cloud security plan from the CSSVs to secure their cloud service. If the cloud service is attacked and loss happens, the users will receive the claim from the CSSVs. To reduce the probability of paying claim, the CSSV has an incentive to invest in improving its cloud security service. To meet these requirements, we propose a two-stage Stackelberg game. In the first stage, the CSSVs jointly set the price charging the users and determine the security investment to improve their offered cloud security service. In the second stage, the users decide on the amount of the cloud security plan to purchase based on the prices of the cloud security plan and the perceived cyber breach probability of the cloud security service. The existence and uniqueness of the equilibrium of the Stackelberg game are proven analytically. Extensive simulations have been conducted to evaluate the performance of the Stackelberg game.

In this paper, we propose secure plaintext image storage protocols in the cloud environment for image owners managing and controlling their outsourced images. To solve control and privacy issues, conventional schemes suggest outsourcing images in the encrypted form. However, encrypted images lose their usability and visibility. For example, image owners cannot quickly find their stored images in the cloud. The proposed protocols encourage plaintext image storage in the cloud with copyright protection of images, visible display, lossless retrieval and controlled deletion via techniques of homomorphic encryption and digital watermarking. We allow the image owner to embed and remove the watermark in a privacy-preserving way without compromising the security of the original data and the computed results. Moreover, the image owner can securely detect the cloud's dishonest act of not completely deleting an image as required or leaking an image without permission. Compared with existing works, our work achieves more functions. We prove that the proposed work achieves the controllable management of the outsourced plaintext images without privacy leakage to unauthorized parties and demonstrate the utility and the efficiency of our protocols through experimental evaluation.

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.

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.

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.

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.

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 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.

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.

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.

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.

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.

Among many biometric technologies, fingerprint identification is the earliest, most pervasively applied, and cheapest biometric technology. It seamlessly corporates integrates sensors, biotechnology, electronic technology, digital image processing and pattern recognition. Fingerprint based identity authentication has expanded from the traditional criminal investigation to e-commerce, attendance, access control, credit card, endowment insurance system and other fields, and has become the popular biometric technology. As for the existing matching algorithm such as feature-point-based or image-based and so on, the accuracy and speed of fingerprint automatic identification are far from the satisfaction of many practical applications. In this work, we investigate the fingerprint feature extraction, and optimize the performance of fingerprint login authentication system by introducing a new feature extractor based on binary image through a brief thinning process. Aiming at the problems of low flexibility, high cost and low efficiency of algorithm in embedded chip of current fingerprint identification system, the automatic fingerprint identification system is comprehensively analyzed. Our proposed method can improve the design and optimization of the automatic fingerprint identification system. Our method can improve the accuracy of fingerprint image recognition in the automatic fingerprint identification system. Meanwhile, it can also improve the adaptability and stability of the automatic fingerprint identification system in various domains. Also, our method can substantially improve the feature extraction of agriculture products. Thereby, the classification task can be enhanced accordingly.

Abstract: Watermarking is a process of embedding a message inside a digital signal like an image, video or text. It is used for several key reasons such as authenticity verification, ownership recognition and hidden communication. In this paper, we discuss about image watermarking, where secret messages are stored in images. Introduction: We propose a dual watermarking approach, which is based on Discrete Cosine Transform, Discrete Wavelet Transform and Singular Value Decomposition methods. This paper considers one watermark as robust and other water mark as fragile. Method: The robust watermark is embedded in Discrete Wavelet Transform- Singular Value Decomposition - domain and is used to transmit hidden messages. The fragile watermark is embedded in Discrete Cosine Transform domain and is used for verification of secret message of the robust watermark. The proposed algorithm is tested in the experimental results section and shows promising results against denoising, rotation, translation and cropping attacks. Result: The results show that the performance of the proposed algorithm in terms of mean squared error, structural similarity and peak signal to noise ratio is S4considerable as compared with the existing methods. Discussion: We present the comparison results with Himanshu et. al. in table 10, from which we can see that our method performs better with gaussian noise and rotational attack only lacking with Salt and Pepper noise. Fig. 7 and Fig. 8, in terms of resulting PSNR shows the variation of noise variance and degree of rotation. From the graphs it is evident that out method performs better against Gaussian and rotational attack. Conclusion: In this paper a dual watermarking method is proposed in which one watermark is fragile which is called as authentication watermark whereas the other watermark is robust and is called as the information watermark. The authentication watermark is embedded in the fractional part of DCT domain in the cover image and the information watermark is embedded in the diagonal vector of the LL sub-band.

The FIDO2 protocol allows users to perform online authentication by setting a public key and avoids the shortcomings of the traditional password authentication mechanism in terms of security. During transaction confirmation with the FIDO2 protocol, users must confirm the transaction message and then sign this message using a cryptographic signature scheme. However, it is a challenge to show that transaction messages are correct or trusted in practice. No available authenticator that supports the FIDO2 protocol uses trusted display hardware to guarantee the correctness of transaction messages. This paper proposes a trusted display of transaction messages by developing a lightweight and trusted base on hardware without a trusted execution environment (TEE). The proposed trusted display is easily applied in the FIDO2 protocol and resists four types of well-known attacks, such as malicious process tampering with display and occupying an authenticator. The experimental results indicate that the improved FIDO2 protocol slightly increases the processing overhead compared to the traditional protocol.

The safety of cloud services within a Trust Model System (TMS) is certainly compromised by a lack of defense against security threats as well as by inaccuracy of the trust results. Our proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats, such as Sybil, on–off, and collusion attacks, by specifying the identity of users and tracking activities undertaken by them in order to easily track unauthorized consumers or attackers and to provide proof of any kind of data leakage. The TMS can also oversee the authorization of whoever uploads feedback into the system. It can also identify invalid feedback and discard it from the system. The algorithms of the TMS first establish a variety of trust criteria in which trustworthiness is calculated. Then, feedback from the cloud service provider nodes is accepted only according to the rules of the TMS. A consumer’s trust value is finally computed using a flexible system capable of guaranteeing a good balance of consumer trust and owners' feedback. Furthermore, a majority of the existing TMS models do not take full account of interaction importance, thus impeding the accuracy of the trust values, a shortcoming that has been rectified in our proposed model.

The design of Data Grids allows grid facilities to manage data files and their corresponding replicas from all around the globe. Replica selection in Data Grids is a complex service that selects the best replica place amongst several scattered places based on quality of service parameters. All replica selection algorithms look for the best replica for the requesting users without taking into account the limitation of their network or hardware capabilities to find the best fit. This leaves capable users with limited ability to connect with the best replica places without fully utilizing their download speed. It furthermore compromises the best replica places and shifts capable users to lower quality replica places and degrades the whole Data Grid environment. To improve quality of service parameters the solution we propose is, a matching algorithm that matches the capabilities of grid user with replica providers that are the best fit. This best-fit approach takes into account both the capabilities of grid users and the capabilities of replica places and creates matches of almost similar capabilities. Simulation results proved that the best-fit algorithm outperforms previous replica selection algorithms.

The concept of cloud computing becomes more popular in latest years. Data storage is very important and valuable research field in cloud computing. Cloud based file sharing is a file sharing security in cloud. The required security from unauthorized access of the file in the cloud is provided by the encryption and decryption function. The admin can provide file access option to the authorized users. This facility limits the number and time of access of the shared files by the admin for the authorized user. Cloud data storage technology is the core area in cloud computing and solves the data storage mode of cloud environment. This project introduces the concept of cloud computing and cloud storage as well as the architecture of cloud storage firstly. Then we analyze the cloud data storage technology amazon web services, wasabi, Digital Ocean etc. We will improve the traditional file storage method and we will make a platform which will get more privileges. Keywords: Cloud, Storage, AWS, Wasabi, File Management, Files Storage, Files Sharing, DMS, CMS, Drive store, Private Cloud.

Biometric based remote authentication has been widely deployed. However, there exist security and privacy issues to be addressed since biometric data includes sensitive information. To alleviate these concerns, we design a privacy-preserving fingerprint authentication technique based on Diffie-Hellman (D-H) key exchange and secret sharing. We employ secret sharing scheme to securely distribute fragments of critical private information around a distributed network or group, which softens the burden of the template storage center (TSC) and the users. To ensure the security of template data, the user’s original fingerprint template is stored in ciphertext format in TSC. Furthermore, the D-H key exchange protocol allows TSC and the user to encrypt the fingerprint template in each query using a random one-time key, so as to protect the user’s data privacy. Security analysis indicates that our scheme enjoys indistinguishability against chosen-plaintext attacks and user anonymity. Through experimental analysis, we demonstrate that our scheme can provide secure and accurate remote fingerprint authentication.

Privacy-preserving outsourced image sharing schemes can help relieve the local storage of image owners and protect images’ privacy by sending encrypted images to receivers, but they cannot prevent the decrypted image from being illegally distributed by illegal distributors. To cope with this issue, we propose a privacy-preserving image sharing scheme with illegal distributor detection (POISIDD). POISIDD embeds the authentication information of image receiver in the encrypted form based on discrete cosine transform (DCT) and privacy-preserving outsourced calculation on floating point numbers (POCF) . Moreover, in order to prevent all operations from being carried out by the single cloud which may lead to privacy disclosure, we adopt two platforms (Storage Center and Authentication Center) to achieve the embedding of authentication information(AI) by the communications between them. Furthermore, in order to better defend against attackers, we generate random vectors based on the original AI and use them instead of the AI as the embedded data. The aim of identifying illegal distributor will be achieved with the help of Authentication Center. The scheme is secure under the defined attack model, and its robustness under different attacks has been shown by experiments.

Authentication is needed for vital applications around the world. Applications that deal with sensitive information need to be kept secret through anyone type of authentication mode. Password-based smart card authentication is a generic model of authentication. Many password-based smart card authentication protocols have been proposed by Sangeetha et al., Giri et al., Jia et al. , Das et al. and so on. Those protocols provide protection against many type of attacks such as offline password guessing attack, server masquerading attack, user impersonation attack, privileged insider attack, but they are open to Crypt analytic attack, Cloning attack, and Reverse engineering attack. This paper proposes an authentication protocol that is termed as hrngRO- hybrid RNG by RO-PUF and SHA-256 for eUASBP simulated using AVISPA tool. It provides Ring Oscillator PUF based random number generator using the SHA-256 hash function. This hybrid combination of RNG makes hrngRO more secure against Crypt analytic attack, Cloning attack, Reverse engineering attack and makes the system even faster. Pre Early Wrong Password Detection is supported by hrngRO using bloom filters. hrngRO provides protection against many types of attacks like other authentication protocols and it also provides additional security by avoiding the above-mentioned attacks. Security of hrngRO is tested using AVISPA tool.

Wi-Fi device authentication is crucial for defending against impersonation attacks and information forgery attacks. Most of the existing authentication technologies rely on complex cryptographic algorithms. However, they cannot be supported well on the devices with limited hardware resources. A fine-grained device authentication technology based on channel state information (CSI) provides a noncryptographic method, which uses the CSI fingerprints for authentication since CSI can uniquely identify the devices. But long-term authentication based on CSI fingerprints is a challenging work. First, the CSI fingerprints are environment-sensitive, which means that the local authenticator should be updated to adapt to the changing channel state. Second, the local authenticator trained with old CSI fingerprints is outdated when users reconnect to the network after being offline for a long time, thus, it needs to be retrained in the access phase with new fingerprints. To tackle these challenges, we propose a CSI-based enhancing Wi-Fi device authentication protocol and an authentication framework. The protocol helps to collect new CSI fingerprints for authenticator’s training in access phase and performs the fingerprints’ dispersion analysis for authentication. In the association phase, it provides packet-level authentication and updates the authenticator with valid CSI fingerprints. The authenticator consists of an ensemble of small-scale autoencoders, which has high enough time efficiency for packet-level authentication and authenticator’s update. Experiments show that the accuracy of the framework is up to 98.7%, and the authenticator updating method can help the framework maintains high accuracy.