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Deep neural networks are vulnerable to adversarial examples, even in the black-box setting where the attacker is only accessible to the model output. Recent studies have devised effective black-box attacks with high query efficiency. However, such performance is often accompanied by compromises in attack imperceptibility, hindering the practical use of these approaches. In this article, we propose to restrict the perturbations to a small salient region to generate adversarial examples that can hardly be perceived. This approach is readily compatible with many existing black-box attacks and can significantly improve their imperceptibility with little degradation in attack success rates. Furthermore, we propose the Saliency Attack, a new black-box attack aiming to refine the perturbations in the salient region to achieve even better imperceptibility. Extensive experiments show that compared to the state-of-the-art black-box attacks, our approach achieves much better imperceptibility scores, including most apparent distortion (MAD), L 0 and L 2 distances, and also obtains significantly better true success rate and effective query number judged by a human-like threshold on MAD. Importantly, the perturbations generated by our approach are interpretable to some extent. Finally, it is also demonstrated to be robust to different detection-based defenses.

We introduce the concept of adaptive trust in crowdsourced IoT services. It is a customized fine-grained trust tailored for specific IoT consumers. Usage patterns of IoT consumers are exploited to provide an accurate trust value for service providers. A novel adaptive trust management framework is proposed to assess the dynamic trust of IoT services. The framework leverages a novel detection algorithm to obtain trust indicators that are likely to influence the trust level of a specific IoT service type. Detected trust indicators are then used to build service-to-indicator model to evaluate a service's trust at each indicator. Similarly, a usage-to-indicator model is built to obtain the importance of each trust indicator for a particular usage scenario. The per-indicator trust and the importance of each trust indicator are utilized to obtain an overall value of a given service for a specific consumer. We conduct a set of experiments on a real dataset to show the effectiveness of the proposed framework., Comment: 14 pages, accepted and to appear in IEEE Ttransactions on Services Computing

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.

Background: Face recognition which belongs to biometric recognition has great application value. Nowadays, face recognition based on deep learning has been widely used in many fields such as internet payment, network login and authentication. However, the face recognition deep learning model are easily replaced and tampered with. Once the models are illegally attacked, it will infringe the intellectual property rights of the model owner and cause economic losses. To deal with these threats, we use watermarking technology to add identity into the face recognition deep learning model. When it is replaced or tampered with, we can prove that the model belongs to us by extracting the watermarks. Objective: In this study, our innovate framework is designed to add watermarks into the face recognition deep learning model as identity, which makes it have features of both trigger sets and data sets. The model will be robust enough to resist common machine learning attacks. With special watermarks, its ownership can be guaranteed. Method: We construct a special watermark trigger set and embed it into the model, which makes it trained without human intervention and annotation. To be flexible for a variety of applications, this scheme uses chaotic sequences to label a watermark trigger set, which guarantees the non-generalization of the watermark. The initial value and parameters used in the method are designed respectively as key to the model. We train 4 models with different number of trigger samples, which is used to study the effect of the number of trigger samples on the model accuracy. Results: We successfully propose a watermarking method for adding identity to the face recognition deep learning model. Watermark extraction rate of the proposed framework is 100%, which means our method can successfully prove ownership of the face recognition deep learning model. In destructive experiments, Models subject to fine-tuning attack still have high face recognition rates which are over 99.00%, and extraction rates of watermarks of each model is 100%. Under overwriting attack, the extraction rates of watermarks of models are less than 25%, models cannot maintain the original performance, which means that watermarks can provide protection until the model loses its ability. The experimental results indicate that the proposed scheme is robust against common machine learning attacks and it prevent the model from being replaced and tempering with. Conclusion: The robustness of the proposed method is capable of resisting machine learning attacks and fine-tuning attacks. It also provides good fidelity, safety, practicality, completeness and effectiveness. With the help of special watermarks, related departments can effectively manage face recognition deep learning models. Besides, it can facilitate the commercialization of intelligent models.

Data integration is a critical step in the analysis of multiple single-cell RNA sequencing samples to account for heterogeneity due to both biological and technical variability. scINSIGHT is a new integration method for single-cell gene expression data, and can effectively use the information of biological condition to improve the integration of multiple single-cell samples. scINSIGHT is based on a novel non-negative matrix factorization model that learns common and condition-specific gene modules in samples from different biological or experimental conditions. Using these gene modules, scINSIGHT can further identify cellular identities and active biological processes in different cell types or conditions. Here we introduce the installation and main functionality of the scINSIGHT R package, including how to preprocess the data, apply the scINSIGHT algorithm, and analyze the output.

Video watermarking technology has attracted increasing attention in the past few years, and a great deal of traditional and deep learning-based methods have been proposed. However, these existing methods usually suffer from the following two challenges: First, most algorithms cannot resist camcorder recording attack, which limits their practical application. Second, watermark embedding may cause substantial degradation of video quality. Through analyzing the unique distortions presented in the camcorder recording process, including geometric distortion, temporal sampling distortion, sensor distortion and processing distortion, this paper proposes a novel spatio-temporal context based adaptive camcorder recording watermarking scheme STACR. In STACR, considering the geometric distortion and video visual quality, we embed the watermark by constructing a spatio-temporal histogram and incorporate a content features based adaptive locating algorithm to select embedding blocks and embedding strengths. As for the temporal sampling attack, we put forward a watermark correlation-based synchronization algorithm and combine it with cross-validation. Moreover, to resist the sensor distortion, we design a local matching-based algorithm to improve the extraction accuracy. In addition, grouped and repeated embedding strategies are combined to cope with the processing distortion. Experimental results compared with the state-of-the-art show that the proposed scheme achieves high video quality and is robust to geometric attacks, compression, scaling, transcoding, recoding, frame rate changes and especially for camcorder recording.

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.

Trust in automation has been mainly studied in the cognitive perspective, though some researchers have shown that trust is also influenced by emotion. Therefore, it is essential to investigate the relationships between emotions and trust. In this study, we explored the pattern of 19 anticipated emotions associated with two levels of trust (i.e., low vs. high levels of trust) elicited from two levels of autonomous vehicles (AVs) performance (i.e., failure and non-failure) from 105 participants from Amazon Mechanical Turk (AMT). Trust was assessed at three layers i.e., dispositional, initial learned, and situational trust. The study was designed to measure how emotions are affected with low and high levels of trust. Situational trust was significantly correlated with emotions that a high level of trust significantly improved participants’ positive emotions, and vice versa. We also identified the underlying factors of emotions associated with situational trust. Our results offered important implications on anticipated emotions associated with trust in AVs.

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.

In recent years, deep learning-based watermarking algorithms have received extensive attention. However, the existing algorithms mainly use the autoencoder to insert watermark automatically and ignore using the prior knowledge to guide the watermark embedding. In this paper, an end-to-end framework based on embedding guidance is proposed for robust image watermarking. It contains four modules, i.e., prior knowledge extractor, encoder, attacking simulator, and decoder. To guide the watermark embedding, the prior knowledge extractor providing chrominance and edge information of cover images is used to modify cover images before inserting the watermark by the encoder. To enhance the robustness of watermark extraction, the attacking simulator applying various differentiable attacks on the encoded images is introduced before extracting the watermark by the decoder. Experimental results show that the proposed algorithm achieves a good balance between invisibility and robustness and is superior to state-of-the-art algorithms.

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.

A source sequence is to be guessed with some fidelity based on a rate-limited description of an observed sequence with which it is correlated. The trade-off between the description rate and the exponential growth rate of the least power mean of the number of guesses is characterized.

Thanks to the eye-tracking sensors that are embedded in emerging consumer devices like the Vive Pro Eye, we demonstrate that it is feasible to deliver user authentication via eye movement biometrics., Demo presented at ETRA '22. Poster and video available at https://www.doi.org/10.18738/T8/WPEXGW

In 2019, the UK's Immigration and Asylum Chamber of the Upper Tribunal dismissed an asylum appeal basing the decision on the output of a biometric system, alongside other discrepancies. The fingerprints of the asylum seeker were found in a biometric database which contradicted the appellant's account. The Tribunal found this evidence unequivocal and denied the asylum claim. Nowadays, the proliferation of biometric systems is shaping public debates around its political, social and ethical implications. Yet whilst concerns towards the racialised use of this technology for migration control have been on the rise, investment in the biometrics industry and innovation is increasing considerably. Moreover, fairness has also been recently adopted by biometrics to mitigate bias and discrimination on biometrics. However, algorithmic fairness cannot distribute justice in scenarios which are broken or intended purpose is to discriminate, such as biometrics deployed at the border. In this paper, we offer a critical reading of recent debates about biometric fairness and show its limitations drawing on research in fairness in machine learning and critical border studies. Building on previous fairness demonstrations, we prove that biometric fairness criteria are mathematically mutually exclusive. Then, the paper moves on illustrating empirically that a fair biometric system is not possible by reproducing experiments from previous works. Finally, we discuss the politics of fairness in biometrics by situating the debate at the border. We claim that bias and error rates have different impact on citizens and asylum seekers. Fairness has overshadowed the elephant in the room of biometrics, focusing on the demographic biases and ethical discourses of algorithms rather than examine how these systems reproduce historical and political injustices., Comment: 14 pages, 3 figures

The principal challenge in biometric authentication is to mitigate the effects of any noise while extracting biometric features for biometric template generation. Most biometric systems are developed under the assumption that the extracted biometrics and the nature of their associated interferences are linear, stationary, and homogeneous. When these assumptions are violated due to nonlinear, nonstationary, and heterogeneous noise, the authentication performance deteriorates. As well, demands for biometric templates are on the rise in the field of information technology, leading to an increase in the vulnerability of stored and dynamic information. Thus, the development of a sophisticated authentication and encryption method is necessary to address these challenges. This dissertation proposes a new Sequential Subspace Estimator (SSE) algorithm for biometric authentication. In the proposed method, a sequential estimator is being designed in the image subspace that addresses challenges arising from nonlinear, nonstationary, and heterogeneous noise. The proposed method includes a subspace technique that overcomes the computational complexity associated with the sequential estimator. In addition, it includes a novel MultiBiometrics encryption algorithm that protects the biometric templates against security, privacy, and unlinkability attacks. Unlike current biometric encryption, this method uses cryptographic keys in conjunction with extracted MultiBiometrics to create cryptographic bonds, called “BioCryptoBond”. To further enhance system security and improve authentication accuracy, the development of a biometric database management system is also being considered. The proposed method is being tested on images from three public databases: the “Put Face Database”, the “Indian Face Database”, and the “CASIA Fingerprint Image Database Version 5.1”. The performance of the proposed solution has been evaluated using the Equal Error Rate (EER) and Correct Recognition Rate (CRR). The experimental results demonstrate the superiority of the proposed method in comparison to its counterparts.

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 scientific research, one of the most significant problems of recent years has been and continues to be the protection of digital material. The advancement of Internet technology has allowed for the illicit duplication, authentication, and distribution of digital material by unauthorized individuals. For this reason, a variety of watermarking systems have been investigated for a variety of purposes, including broadcast monitoring, intellectual property protection, content authentication, and copy control. There are various types of the watermarking image attacks that impact the quality of the images; therefore, it is critical to ensure that watermarked digital images can withstand these kinds of attacks. Hence, novelty of the proposed research is to develop approaches to detect these attacks which becomes very important to guarantee a sufficient quality of watermarking images. In this paper, a deep learning method based on a convolution neural network (CNN) algorithm was proposed to detect various types of watermarking attacks, namely, median filter, Gaussian filter, salt-and-pepper, average filter, motion blur, and no attack, to improve the watermarking quality. Evaluation metrics such as peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and the normalization correlation (NC) were employed to examine the invisibility and robustness of the watermarking images. The empirical results of the CNN model show good performance for detecting watermarking attacks with different sizes (256, 128, and 64). The accuracy percentage of the testing process was 98%. A highly efficient CNN approach was developed. Very high performance of NC was found in the detection of the salt-and-pepper attack (99.02%, 99.97%, and 99.49% with respect to watermarking image sizes of 256 × 256, 128 × 128, and 64 × 64, respectively). The study concludes that the CNN model is able to detect watermarking attacks successfully.

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.

The use of cloud technology has increased in many businesses.Cloud providers should handle privacy and security concerns as a top priority. Customers are increasingly less interested in dealing with "individual cloud" suppliers due to potential issues such as availability and performance failure and the threat of hostile intruders in the individual cloud. This study focuses on the problems regarding cloud computing's data security. Usage of cloud computing wish to avoid using an unscrupulous cloud provider because their information and data will be transferred with a third party. Protecting sensitive and confidential information from attackers or malicious insiders. Keywords: Security, Individual cloud, Multi-cloud.

The widespread use of Internet of Things (IoT) and Data Fusion technologies make privacy protection an urgent problem to be solved. The aggregated datasets generated in these two scenarios face extra privacy disclosure. We define attribute sets with different sources in an aggregated dataset as quasi-sensitive attribute sets (QS sets). The QS set itself is not sensitive, but internal linking attacks may occur when two QS sets in an aggregated dataset are linked. In this paper, we propose a new privacy model, namely, the QS k-anonymity model. The QS k-anonymity model is effective in preventing internal linking attacks. We provide two algorithms for the QS k-anonymity model, the Greedy QS k-anonymity algorithm and the Efficient QS k-anonymity algorithm. We evaluate our algorithms on real datasets. The experimental results show that the Greedy QS k-anonymity algorithm has good data utility, the Efficient QS k-anonymity algorithm shows better efficiency, and both algorithms are well scalable.

Our Project is to develop the technique for fingerprint authentication in ATMs. This target can be mainly decomposed into image preprocessing, feature extraction and feature match. For each sub-task, some classical and up-to-date methods in literature are analyzed. Based on the analysis, an integrated solution for fingerprint recognition and authentication is developed for demonstration. Our demonstration program is coded in ASP.NET & MATLAB. For the program, some optimization at coding level and algorithm level are proposed to improve the performance of my fingerprint authentication system. These performance enhancements are shown by experiments conducted upon a variety of fingerprint images. Also, the experiments illustrate the key issues of fingerprint recognition that are consistent with what the available literatures say.

Data is a crucial resource for every business, and it must be protected both during storage and transmission. One efficient way of securing data and transferring it is through digital watermarking, where data is hidden inside a medium like text, audio, or video. Video watermarking is visible or invisible embedded data on a video in a logo, text, or video copyright disclaimer. In this proposed paper, the goal is to analyze the characteristics of video watermarking algorithms and the different metrics used for them. It deals with the extent to which the different requirements can be fulfilled, taking into consideration the conflicts between them and the practical challenges of video watermarking in terms of attacks like geometric attacks and non-geometric attacks. It also focuses on the process of watermarking a video. Recent advances in data security indicate that employing a video watermarking technology to transmit private data will be an effective method of transmitting sensitive data.

Digital image watermarking is used to secure the digital images. There are several methods for securing the images. But there is probability to destroy the information by an attacker. Attacker can damage even a single segment of watermark image. There are many techniques to overcome from several attacks through which the cropped section of an image can be recover and there is no need to detect watermark. A robust image watermarking method against various cropping attack by the use of Sudoku puzzle is suggested in this paper. This method is build upon the attributes of permutation of Sudoku matrix and it gives Sudoku table has 9x9 formats, i.e. uniformly divided into multiple sample of images in the sections of the original image. Justified Sudoku result is explained for the embedded and extraction of the watermark image. The standard format of Sudoku having 9x9 matrix is used and the method expressed the hardiness up to great extent against crop attack.

Monero is one of the prominent cryptocurrencies bringing robust privacy safeguard levels. However, for Monero lacking accountability, it is easy to use anonymity and privacy for committing the crime without the leakage of identity. And some exchanges are not very receptive to Monero. The purpose of this study is to balance privacy and accountability in Monero. Specifically, we studied the way to provide accountability while keeping privacy. We develop an accountable Monero model. Our model isolates three kinds of roles, users, the trusted registration authority, and a trusted regulator. Accountability enables the trusted regulator to reveal the signer’s identity. Only the trusted regulator can trace users’ public keys as needed. We give a construction for the accountable Monero system by combining CryptoNote protocol with ElGamal encryption. Our instantiation is with a marginal influence on efficiency. The security of our scheme is based on the discrete logarithm and decisional Diffie–Hellman assumption.

A good website can have a significant impact on the e-commerce business. As a result, variables that improve an e-commerce website must be considered. The colour of a website is a crucial factor that has a direct impact on influencing people minds. Many studies have been conducted to better understand how colour is used in various fields to reap the benefits. In this study, the major purpose is to explore key colours and their impact on the website design to identify how colour affects generation z in an e-commerce website. Primary and secondary colours are examined in the analysis. A questionnaire will be created, and an online survey of Generation Z will be conducted. The collected data may be analysed to determine the most affecting colours for website design among generation Z, as well as the likability of various colours among generation Z. This knowledge will aid in making better colour choices for Generation Z in terms of website design and marketing.

With the rapid popularization and development of location-based services (LBS), location privacy preserving has become a mainstream concern. Existing anonymity-based methods rely mainly on anonymous location selection based on geographic information, which neglects the semantic information on the location, and behavior of self-interested users may raise a privacy vulnerability. A blockchain-based approach for location privacy protection schemes has been proposed to address these issues. In this scheme, the privacy level of the location is improved using private chain-based collaborative anonymous communication with anonymous set construction methods that consider the semantic diversity associated with the related user. Compared with other existing methods, evaluation based on real-world datasets shows that the proposed method prevents location privacy leakage from collaborative users and constructs a semantical anonymity set, thus effectively protecting the user’s location privacy.

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.

A user's identity ensures by the process of Authentication. Authentication is an important security service provided to the system by the different authentication algorithms. The algorithm includes Text password, Graphical password, Biometric password, Token authentication, etc. Text password mostly includes simple text like names, etc. And text passwords can be easily cracked. Biometric identifiers include fingerprints, etc. In Graphical password, the user selects images in a specific order, presented in a GUI. And tokens are smart cards like credit cards etc. But cards can be stolen, and some people don't want to carry their cards. Therefore, the secure authentication scheme introduced called 3D PASSWORD". It is a combination of all these schemes that is Recognition + Recall + Token + Biometric. 3D password is a multi-factor authentication technique that has a virtual environment that looks like a real-time environment, but it is not a real-time environment. It is more secure than other schemes of authentication.

Biometric authentication is now extensively used in several systems and applications to authenticate users using their biometrics. The biometrics of the users are compared to the biometric templates already stored on the server, and if there is a match, only the user is permitted to enter the system. However, because each user's biometrics are unique, it is more important than the user's actual biometric data is never leaked. Moreover, the utilization of the user's actual biometric data for comparisons during the authentication process can't be done because the revelation of the user's actual biometrics to the server should not be done. Throughout authentication, each user will encrypt his biometrics and then transmit this encrypted data to the server for comparison, and this data will never be decrypted for privacy reasons during the whole authentication process. To compare two encrypted data without decrypting them, the present study uses the homomorphic properties of the Pailler cryptosystem which will be the encryption of the algorithm for the comparison part. The use of Euclidean Distance is made to find the squared distance between the users’ queried feature vector and the templates stored into the server. In the end, among all the distances, the minimum distance will be chosen and will compare with some predefined threshold to decide whether the user is an authenticated user or not.

Data hosting on cloud decreases cost of IT maintenance and data reliability get enhance. Nowadays, customers can store their data on single cloud, which has some drawbacks. First is vendor lock in problem and second is security on cloud. The solution to this problem is to store the data on different cloud server without redundancy using encryption algorithm. Customers do not want to lose their sensitive data on cloud. Another issue of cloud computing is data thievery should be overcome to supply higher service. Multi-cloud environment has ability to scale back security risks. To avoid security risk we offer framework. Keywords: Cloud computing, cloud storage, data hosting, data intrusion, multi-cloud, single cloud.

For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way in latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localisation accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room and a large office space, demonstrate that use of light spectral information significantly reduces the localization error for the different percentiles., Comment: 14 pages, 17 figures, 4 tables

With the development of mobile communication, while users enjoy higher-value services, they are always concerned about whether their privacy is leaked. Therefore, it is very important to ensure anonymity in the service process. This paper briefly introduces the importance of anonymous authentication, summarizes the commonly used techniques of anonymous authentication such as group signature and ring signature, summarizes the typical anonymous authentication schemes and some existing problems, and points out that anonymous authentication will become the focus of research.

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

With the rapid spread of network information, the information maintenance has become the focus of information security on networks. Digital watermarking is one of the effective methods to protect information security, achieve anticounterfeiting traceability, and protect copyright, and it is an important branch of information hiding technology. However, one of the most challenging questions of digital watermarking is how to present strong robustness in geometric attacks. Nowadays, most watermarking algorithms are relatively weak robustness against geometric attacks. A robust watermarking algorithm against geometric attacks based on the non-subsampled shearlet transform and the Harris-Laplace detector is proposed. The host image is decomposed into subbands with different directions by the shearlet transform, and the Harris-Laplace detector is utilized to obtain the feature regions. Then, the nonoverlapping regions with strong robustness are selected to embed watermark by the fuzzy c-means cluster algorithm. The experimental results indicate that the proposed watermarking scheme can well resist geometric attacks.

Biometric recognition may be used in conjunction with human authentication on a smartphone to improve accuracy, reliability, and simplicity, and to aid in fraud prevention and user authentication. While single biometric authentication addresses environmental degradation and sensor noise limitations, and the single point of failure scenario in biometric systems can result in more robust biometric systems, multimodal biometric authentication can improve the accuracy of identification and recognition. The purpose of this research is to propose a facial and speech authentication system that is cloud-based and supports a web-based examination approach. The system enables students' biometrics to be registered, students to be recognized, and student recognition results to be reported. The confusion matrix is used to compare the results of positive and negative detection in various ways, including accuracy score, precision value, and recall value. Adaptive multimodal biometric authentication should be designed and evaluated for further research using the optimal weights for each biometric.

This paper deals with the problem of robust and perceptual logo watermarking for colour images. In particular, we investigate trade-off factors in designing efficient watermarking techniques to maximize the quality of watermarked images and the robustness of watermark. With the fixed size of a logo watermark, there is a conflict between these two objectives, thus a multiobjective optimization problem is introduced. We propose to use a hybrid between general regression neural networks (GRNNs) and multiobjective memetic algorithms (MOMA) to solve this challenging problem. Specifically, a GRNN is used for efficient watermark embedding and extraction in the wavelet domain. Optimal watermark embedding factors and the smooth parameter of the GRNN are searched by a MOMA for optimally embedding watermark bits into wavelet coefficients. The experimental results show that the proposed approach achieves robustness and imperceptibility in watermarking.

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