With the rapid growth of Android applications, ensuring robust security has become a critical concern. Traditional Vulnerability Assessment and Penetration Testing (VAPT) approaches, though effective across platforms, often fall short in addressing Android-specific security challenges. This paper presents a comprehensive review of security testing methods tailored to the Android ecosystem, including static and dynamic analysis, hybrid approaches, network communication testing, reverse engineering, malware detection, and permission-based assessments. Android's open-source nature, device fragmentation, and inconsistent security policies introduce unique vulnerabilities that require specialized testing strategies. By examining current tools, methodologies, and best practices, this review identifies recurring gaps in the Android application security testing process. It highlights the need for more adaptable and thorough testing frameworks. The insights provided are valuable to developers, researchers, and security professionals aiming to strengthen Android app security. Ultimately, this work underscores the importance of tailoring security assessment practices to the evolving threat landscape of the Android platform, thereby contributing to the development of safer and more resilient applications.

The dark web is a collection of hidden content and web sites hosted on the darknet, which is not indexed by standard search engines and can only be accessed using specialized browsers like Tor, JonDonym, and I2P. As Internet technology advances, the threat to personal data security grows correspondingly, making the dark web a hub for malicious activities such as bank fraud, data theft, and security breaches. The content on the dark web is deliberately concealed from normal users, and its anonymity makes it a haven for illicit activities. Therefore, monitoring the darknet is crucial to detect data breaches and prevent serious consequences. Traffic classification plays a vital role in various areas such as security, service management, and research and development. In this experiment, traffic from dark web anonymity tools (Tor, JonDonym, and I2P) is classified at different levels of granularity, including network, traffic, and application levels. Initially, dark web traffic classification is conducted using four machine learning classifiers: naive Bayes, multinomial naive Bayes, decision tree, and random forest, utilizing a publicly available dataset. The impact of class imbalance within the dataset is also examined experimentally, employing the Synthetic Minority Oversampling TEchnique (SMOTE) to address the imbalance. Following this, the effectiveness of a neural network, specifically a multilayer perceptron, is evaluated for the classification task, and its performance is compared against the aforementioned classifiers.

The integration of PSO with CNN provides a promising approach for classifying ASD using sMRI data. ASD is a behavioral disorder that impacts a person’s lifetime tendency to reciprocate with society. The variability and intensity of ASD symptoms, in addition to the fact that they share symptoms with other mental disorders, make an early diagnosis difficult. The key limitation of CNN is selecting the best parameters. To overcome this, we use PSO as an optimization approach within the CNN to choose the most relevant parameters to train the network. In the proposed approach, we initialize a swarm of particles, where each particle represents a unique configuration of CNN hyperparameters, including the number of convolutional layers, learning rates, filter sizes, and batch sizes. To evaluate the swarm in PSO, we use a fitness function, such as accuracy, to measure each particle’s performance. The performance of the proposed approach for ASD prediction outperformed that of the other optimizers with a high convergence rate.

Machine learning has shown significant potential in medical diagnosis, particularly for Alzheimer's disease, which accounts for 60–70% of dementia cases. However, traditional machine learning models rely on centralized data collection, raising concerns about privacy and security, especially when dealing with sensitive medical information. To address these concerns, Federated Learning (FL) has emerged as a promising solution. FL enables collaborative model training across multiple devices or institutions without the need to share raw data, thereby enhancing data privacy. Although Federated Learning offers privacy benefits through decentralized model training, breaches can still occur during the transmission of parameters to the central server. Attackers may infer sensitive information from shared model parameters, compromising data privacy through reconstruction attacks. To mitigate this vulnerability, Homomorphic Encryption (HE) is employed as a safeguard, allowing secure computations on encrypted data without revealing the underlying information. This paper presents a Federated Learning technique enhanced with Homomorphic Encryption for the detection of Alzheimer’s disease, ensuring both accuracy and robust privacy protections. The approach utilizes a publicly available Alzheimer’s dataset sourced from GitHub. The study evaluates the performance of this technique using three machine learning algorithms: Decision Tree, Random Forest, and Logistic Regression. Among these, Logistic Regression demonstrated the highest accuracy, achieving 87.44%.

Accurate estimation of battery state including state of charge (SoC) and state of health (SoH) are crucial for ensuring safety in energy storage applications. The SOC and SOH estimators were independently trained using the same input vector but with different objective functions, no integration between SOC and SOH estimations were explored. In this paper, a unified algorithm, for identifying both SoC and SoH states, is introduced by considering the Bayesian optimization for hyperparameter tuning. This approach allows seamless transition between SoC and SoH estimation without needing separate models for each task. In addition, equipping the dual estimation framework with a unified algorithm for identifying both states would impact the algorithm's complexity. The suggested BiLSTM model reduces complexity in real-time Battery Management System (BMS) applications by eliminating the need for a separate model to estimate SoH. When compared to other machine learning and deep learning models such as Support Vector Machines (SVM), Decision Trees (DT), Random Forest (RF), Radial Basis Function Neural Networks (RBF-NN), Recurrent Neural Networks (RNN), and LSTM, the suggested BiLSTM method demonstrates the highest efficiency. Finally, to verify the proposed method's effectiveness, a comparison among the different evaluation metrics was conducted. The proposed BiLSTM model achieved an average MAE (Mean Absolute Error) of 0.08 and NRMSE (Normalized Root Mean Squared Error) of 0.15 for SoC estimation across various temperatures (5°C, 15°C, 35°C, and 45°C), and an MAE of 3.12 and NRMSE of 0.23 for SoH estimation with a degradation rate of 47% of the cell estimated from the predicted capacity values.

Many firms have outsourced most of their outsourcing tasks to third parties in recent years. However, traditional outsourcing procedures have challenges like centralization, data security, complex payment systems, time-consuming and costly dispute resolution methods, and the need for more trusted real-time tracking and monitoring systems. This chapter delves into the paradigm shift from traditional outsourcing methods to new trustless and decentralized Blockchain-based outsourcing methods. Blockchain, with its core principles of decentralization, immutability, and transparency, is a primary candidate to solve the challenges of traditional outsourcing methods. The main objective of this chapter is to explore the benefits of integrating Blockchain in outsourcing of supply chain operations and management science methods. Moreover, the chapter also investigates some real-time industrial use cases and research works. The chapter discusses the challenges in integrating Blockchain, potential solutions, and future research directions.

Autism spectrum disorder (ASD) is a complex neuro-developmental disorder distinguished by challenges in communication, social interaction and repetitive behavior. It is essential to diagnose ASD early to ensure timely intervention and support, which may substantially improve the outcomes for autistic people. Researchers explored deep learning (DL) and pre-trained models to predict autism. However, most of the existing deep learning approaches use convolution-based models which fail to extract spatial information and handle complex patterns, resulting in the extraction of low intricate features from images. To overcome this, we propose a novel approach to predict autism using involutional neural networks and machine learning techniques (INN-ASDNet), which are designed to mitigate the parameter-intensive nature of convolutional neural networks. INN-ASDNet uses the involution operation to extract the features and random forest classifier to classify the ASD. Involution kernels are intended to be location and channel specific, in contrast to convolution kernels, which are channel- and spatially-agnostic. A location-specific feature enhances the network’s capacity to identify complex information in medical images by enabling it to adjust to various visual patterns based on spatial locations. In contrast to DL approaches, INN-ASDNet typically requires fewer parameters since the filters are generated dynamically for each position, leading to potentially lower memory usage and faster computation. INN-ASDNet is tested on the ABIDE I dataset, and the efficiency of the system is assessed using performance metrics such as accuracy, recall, precision, and F1-score. Experimental results conclude that INN-ASDNet outperforms the DL-based approaches and achieves an accuracy of 98.18%.

The utilization of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially in recent years over traditional authentication systems. Since the biometric traits are irrevocable, two important issues such as security and privacy still need to be addressed in BAS. Researchers explore homomorphic encryption (HE) to propose several privacy-preserving BAS. However, the correctness of the evaluated results computed by the cloud server on the protected templates is still an open research challenge. These methods are able to conserve the privacy of biometric templates but unable to check the correctness of computed result results in false reject or accept. To overcome this issue, we suggest a reliable and privacy-preserving verifiable multi-instance iris verification system using Paillier HE and one-digit checksum (PVMIAPO). Modified local random projection is implemented on the fused iris template to produce the reduced template. Later, Paillier HE is applied on the reduced template to create the protected template. The result returned by the third party server is verified using the one-digit checksum. The efficiency of PVMIAPO is verified by experimenting with it on SDUMLA-HMT, IITD, and CASIA-V3-Interval iris databases. PVMIAPO gratifies the irreversibility, diversity, and revocability properties. PVMIAPO also obtains fair performance in contrast to the existing methods.

In blockchain technology, mining refers to the process of validating new transactions and adding them to a permanent public ledger called distributed ledger technology (DLT). While solo mining becomes challenging for individual miners due to limitations of solo miner's hash rate and reward consistency, miners typically opt to join mining pools to combine resources and increase reward consistency. Mining pools have significantly impacted the Proof of Work (PoW) consensus, which relies heavily on decentralized mining pools to secure and validate transactions on a blockchain network. It necessitates miners to solve complex mathematical puzzles to add new blocks to the blockchain. However, it is imperative to address the potential drawbacks of PoW consensus. Establishing trust among miners in PoW mining pools is challenging due to the inherent risks associated with the decentralized nature of Blockchain. In this study, a decision management system is developed by leveraging the characteristics of Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), termed Strategic Miner Selection with TOPSIS (SMNST). Firstly, this SMNST evaluates the miner's ranks based on the decision criteria of the dynamic blockchain network. Secondly, the choice of block generation is further optimized through ranked miners only to reach an ideal solution in the pool consensus. Simulation results were demonstrated using Bitcoin Testnet3, and the effectiveness of miners in mitigating risks in PoW mining pools was analyzed through hash rate, latency, pool up-time, and block propagation time metrics.

Many applications widely use broadcast communications (BC) due to their efficiency in simultaneously distributing data to many receivers. More specifically, BC is essential in resource-constrained devices (RCDs) for conserving energy and bandwidth, which are vital for increasing the longevity and performance of RCDs. The three major challenges in BC are source authentication, data integrity and continuous authentication (CA). The first two challenges are effectively addressed in TESLA and its variants. However, achieving CA without incurring overheads on the sender and receiver sides remains an unresolved challenge. In this paper, we propose Infinite (Inf) TESLA++, assisted by Blockchain and smart contracts, to address the CA challenge with minimal overhead at sender and receiver. Our theoretical and simulation analysis demonstrates that Inf-TESLA++ incurs minimal overhead compared to existing protocols while eliminating the need for direct communication between the sender and receiver for synchronization.

Data centers have become an indispensable part of modern computing infrastructures. It becomes necessary to manage cloud resources efficiently to reduce those ever-increasing power demands of data centers. Dynamic consolidation of virtual machines (VMs) in a data center is an effective way to map workloads onto servers in a way that requires the least resources possible. It is an efficient way to improve resources utilization and reduce energy consumption in cloud data centers. Virtual machine (VM) consolidation involves host overload/underload detection, VM selection, and VM placement. If a server becomes overloaded, we need techniques to select the proper virtual machines to migrate. By considering the migration overhead and service level of agreement (SLA) violation, we investigate design methodologies to reduce the energy consumption for the whole data center. We propose a novel approach that optimally detects when a host is overloaded using known CPU utilization and a given state configuration. We design a VM selection policy, considering various resource utilization factors to select the VMs. In addition, we propose an improved version of the JAYA approach for VM placement that minimizes the energy consumption by optimally pacing the migrated VMs in a data center. We analyze the performance in terms of energy consumption, performance degradation, and migrations. Using CloudSim, we run simulations and observed that our approach has an average improvement of 24% compared to state-of-the-art approaches in terms of power consumption.

Human beings can describe scenarios and objects in a picture through vision easily whereas performing the same task with a computer is a complicated one. Generating captions for the objects of an image helps everyone to understand the scenario of the image in a better way. Instinctively describing the content of an image requires the apprehension of computer vision as well as natural language processing. This task has gained huge popularity in the field of technology and there is a lot of research work being carried out. Recent works have been successful in identifying objects in the image but are facing many challenges in generating captions to the given image accurately by understanding the scenario. To address this challenge, we propose a model to generate the caption for an image. Residual Neural Network (ResNet) is used to extract the features from an image. These features are converted into a vector of size 2048. The caption generation for the image is obtained with Long Short-Term Memory (LSTM). The proposed model is experimented on the Flickr8K dataset and obtained an accuracy of 88.4%. The experimental results indicate that our model produces appropriate captions compared to the state of art models.

Everyday dealing with enormous amounts of sensitive data requires its protection and communication over the insecure network. The field of ‘Steganography’ always attracted researchers for significant amount of scientific attention to protect and communicate sensitive data. This paper presents a secure steganography scheme for hiding Gray-scale secret image into a Color cover image by replacing cover image bits in frequency domain using modified quantum substitution box (S-Box). The inclusion of modified quantum S-Box for concealing secret bits in randomly selected any of the two channels of cover image ensures enhanced security. In the proposed scheme, we first performed discrete cosine transform (DCT) on the cover image. Then, quantum S-box is applied to locate the position of DCT coefficients where least significant bits are substituted intelligently based on the relative ordering of DCT frequencies. This relative ordering is achieved by traversing DCT coefficients in a zigzag manner where less important pixels have been altered more effectively without any major loss in image quality. The security of proposed method is examined by key space, key sensitivity parameters and robustness analysis. Additionally, the conducted simulation results demonstrate that our proposed steganography scheme has better visual image quality in terms of MSE, PSNR, UQI, SSIM, RMSE parameters as compared to other state-of-the-art works.

The use of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially over traditional authentication systems in recent years. Multimodal BAS was introduced to solve unimodal BAS's difficulties and improve security. Privacy and security are two significant concerns to be addressed in BAS, as biometric traits are irrevocable. Researchers employed cancelable biometrics in the past few years to propose several privacy-preserving BAS. We propose a privacy-preserving bimodal cancelable BAS (PPBCBAS) to overcome these problems. The traits used in our method are iris and fingerprint. Features are extracted from both the traits, and feature level fusion is done by concatenating the feature vectors of iris and fingerprint. PPBCBAS uses a quotient filter to generate the cancelable template, and the comparison is made on these transformed templates using the modified Hamming distance. PPBCBAS has been tested on three publicly available databases to analyze its efficiency. PPBCBAS satisfies the diversity, irreversibility, and revocability properties and achieves decent performance.

The requirement of a person to be available during the authentication made the biometric authentication to be used in abundant applications over password or token-based authentication systems. Privacy and security are two major concerns still to be addressed in biometric authentication system. In the last couple of years, researchers used the homomorphic encryption (HE) to propose the privacy-preserving biometric authentication systems which overcomes the limitations of cancelable biometrics and biometric cryptosystems. But these methods fail to achieve overall performance and security measures. To handle this, we introduce a privacy-preserving Bimodal authentication system (PPBA) utilizing Fan-Vercauteren scheme. An optimized method is proposed to compute the hamming distance between the encrypted templates that helps to carry out the computation without disclosing the user sensitive data. PPBA is tested on publicly available databases to analyze its efficiency. PPBA satisfies the diversity, irreversibility, revocability properties and also achieves decent performance.

Many government and commercial organizations are using biometric authentication systems instead of a password or token-based authentication systems. They are computationally expensive if more users are involved. To overcome this problem, a biometric system can be created and deployed in the cloud which then can be used as a biometric authentication service. Privacy is the major concern with cloud-based authentication services as biometric is irrevocable. Many biometric authentication systems based on cancelable biometrics are developed to solve the privacy concern in the past few years. But the existing methods fail to maintain the trade-off between speed, security, and accuracy. To overcome this, we present a multi-instance cancelable iris system (MICBTDL). MICBTDL uses a convolutional neural network trained using triplet loss for feature extraction and stores the feature vector as a cancelable template. Our system uses an artificial neural network as the comparator module instead of the similarity measures. Experiments are carried on IITD and MMU iris databases to check the effectiveness of MICBTDL. Experimental results demonstrate that MICBTDL accomplishes fair performance when compared to other existing works.

Programming Skills play a crucial role in any computer engineering student's life to apply the concepts in solving any real world problem as well to crack a secure job in the dream company. To achieve this they should assess their performance in programming, analyze and improve their skills regularly. Many students are even undergoing mental stress and depression and even attempting suicides out of the stress if the considered scores and performance are not met. With the help of analyzing the programming skills one can enhance their scores and performance on a regular basis, introspect and can deliberately practice for better improvement. This reduces the stress, anxiety and depression on students' minds in securing good scores in their academics and in building their career to achieve the goal. This analysis helps even professors to improvise the teaching and learning outcomes of students and increase their performance in whichever field they are working in. We made a comparison of different machine learning algorithms based on 200 classification instances. This analysis helped us in analyzing the statistics of students' performance.

In this digital age, many learning technologies and tools are suitable for synchronous and asynchronous learning. There is an interaction between participants, instructor, and training in synchronous learning at fixed timing. In synchronous learning, there is real interaction between participants. In asynchronous learning, there is no real-time interaction between the participants. Students can learn at their own time and pace. So, in asynchronous learning, there is a need to understand whether the learner has understood the concepts. The evaluation can be achieved using H5P, an interactive course content creation tool. This study aims to measure the learning outcomes by making the students understand the concepts through the active learning experience. The learning enhancement is achieved by creating interactive content through H5P. The learners can study through the interactive content and revise the concept using the engagement, which leads to improved performance in their end exams. The participants included 60 engineering students of IV B. Tech Information Technology at a women-only engineering educational institution. The participants are allowed to watch prerecorded self-made videos, participate in activities like quiz at a particular duration of the video, and get feedback immediately. Summaries were also added at the end of the videos. The course instructor gets the report of all students’ participation status and scores of the entire class in the LMS platform Moodle. H5P helps the instructor understand the students’ learning difficulties, and it will be addressed enabling the attainment of improved learning outcomes.

Diabetic retinopathy is a common diabetic disease that affects the retina and can result to blindness if not treated initially. Deep learning (DL)-based models are proposed to detect the blood abnormalities in the retinal tissue due to diabetes mellitus obtained from fundus camera. The drawback with these models is the lack of performance. To address this, we propose to automate the process of detection of severity of diabetic retinopathy (DR) using ensembles of pretrained models, thus exploring the power of transfer learning in the field of automated diagnosis. Deep learning models perform well when the model is trained on a large amount of data. In this regard, we also put forth data augmentation and preprocessing techniques to generate the synthetic images and to improve image quality. Extensive experimental results on publicly available database illustrate that the proposed ensemble model achieves fair accuracy when compared to existing models. Thus, the proposed model shows good scope for deployment in real-time diagnosis.

Homomorphic encryption (HE) is the most widely explored research area in the construction of privacy-preserving biometric authentication systems because of its advantages over cancellable biometrics and biometric cryptosystems. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save computational resources, resulting in a false accept/reject. To address this, secure and verifiable multi-instance iris authentication using blockchain (SviaB) is proposed. Paillier HE provides confidentiality for the iris templates in SviaB. The blockchain offers the integrity of the encrypted reference iris templates as well as the trust of the comparator result. The challenges of using blockchain in biometrics are also addressed in SviaB. Extensive experimental results on benchmark iris databases demonstrate that SviaB provides privacy to the iris templates with no loss of accuracy and trust in the comparator result.

Lattice-based crypto systems are regarded as secure and believed to be secure even against quantum computers. lattice-based cryptography relies upon problems like the Shortest Vector Problem. Shortest Vector Problem is an instance of lattice problems that are used as a basis for secure cryptographic schemes. For more than 30 years now, the Shortest Vector Problem has been at the heart of a thriving research field and finding a new efficient algorithm turned out to be out of reach. This problem has a great many applications such as optimization, communication theory, cryptography, etc. This paper introduces the Shortest Vector Problem and other related problems such as the Closest Vector Problem. We present the average case and worst case hardness results for the Shortest Vector Problem. Further this work explore efficient algorithms solving the Shortest Vector Problem and present their efficiency. More precisely, this paper presents four algorithms: the Lenstra-Lenstra-Lovasz (LLL) algorithm, the Block Korkine-Zolotarev (BKZ) algorithm, a Metropolis algorithm, and a convex relaxation of SVP. The experimental results on various lattices show that the Metropolis algorithm works better than other algorithms with varying sizes of lattices.

With the escalated usage of a biometric authentication system (BAS), template protection for biometrics attracted research interest in recent years. The assumption behind the existing homomorphic encryption-based BASs is that the server performs the computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources, which may result in false accept/reject. To tackle this challenge, we propose a secure and verifiable classification based iris authentication system (SvaS). SvaS aims to achieve both privacy-preserving (PP) training and PP classification of Nearest Neighbor and Multi-class Perceptron models. The Fan-vercauteren scheme provides confidentiality for the iris templates, and aggregate verification vector helps to verify the correctness of the computed classification result. Extensive experimental results on benchmark iris databases demonstrate that SvaS provides privacy to the iris templates with no loss in accuracy and eliminates the need to trust the server.

Homomorphic Encryption (HE) is the most widely explored research area to construct privacy-preserving biometric authentication systems due to its advantages over cancelable biometrics and biometric cryptosystem. However, most of the existing privacy-preserving biometric authentication systems using HE assume that the server performs computations honestly. In a malicious server setting, the server may return an arbitrary result to save the computational resources results in false accept/reject. To address this, we propose a privacy-preserving and verifiable multi-instance iris authentication using public auditor (PviaPA). Paillier HE provides confidentiality for the iris templates in PviaPA. A public auditor ensures the correctness of comparator result in PviaPA. Extensive experimental results on benchmark iris databases demonstrate that PviaPA provides privacy to the iris templates with no loss in the accuracy as well as trust on the comparator result.

Rapid advancement in technology has led to the use of biometric authentication in every field. In particular, from the past few years, iris recognition systems has gained overwhelming advancement over other biometric traits due to its stability and uniqueness. Directly storing the templates into a centralized server leads to privacy concerns. Many state-of-the-art iris authentication systems based on cancelable biometrics and bio-cryptosystems have been introduced to provide security for the iris templates. However, these works suffer from accuracy loss relative to unprotected systems, or they require auxiliary data (AD), which compromise the privacy of the templates and security of the system. To address this, we propose a novel privacy-preserving iris authentication using fully homomorphic encryption which ensures the confidentiality of the templates and restricts the leakage of data from the templates. Our method improves the recognition accuracy by generating rotation invariant iris codes and reduces the computational time by using the batching scheme. Our approach satisfies all the requirements specified in the ISO/IEC 24745 standard. The proposed method has experimented on four benchmark publicly available iris databases which illustrate that our method can be practically achievable with no loss in the accuracy and preserve the privacy of the iris templates. Our method encrypts and computes the Hamming distance of 2560-dimensional iris features in about 0.0185 seconds only with an equal error rate value of 0.19% for CASIA-V 1.0 database.

Multi-biometric systems have been widely accepted in various applications due to its capability to solve the limitations of unimodal systems. Directly storing the biometric templates into a centralised server leads to privacy concerns. In the past few years, many biometric authentication systems based on homomorphic encryption have been introduced to provide security for the templates. Most of the existing solutions rely on an implication of the assumption that the server is 'honest-but-curious'. Therefore, the compromise of server results into the entire system vulnerability and fails to provide the integrity. To address this, we propose a novel multi-instance iris authentication system, BMIAE to deal with malicious attacks over the transmission channel and at the untrusted server. BMIAE encrypt the iris templates using ElGamal encryption to guarantee confidentiality and Smart contract running on a Blockchain helps to achieve the integrity of templates and matching result. BMIAE also addresses the limitations of using Blockchain for biometrics like privacy and expensive storage. To check the effectiveness and robustness, BMIAE has experimented on CASIA-V3-Interval, IITD and SDUMLA-HMT iris databases. Experimental results show that BMIAE provides improved accuracy, and eliminates the need to trust the centralised server when compared to the state-of-the-art approaches.

In recent years, biometric authentication system (BAS) has become the most promising and popular authentication system in identity management. Due to its capability to solve the limitations of unimodal systems, multi-biometric systems (MBS) have been extensively accepted in various fields. The main step in MBS is information fusion. On the other hand, directly storing the fused templates into a centralized server leads to privacy concerns. Recently, many BAS based on homomorphic encryption has been introduced to provide confidentiality for the fused templates. However, most of the existing solutions rely on an implication of the assumption that the server is “Honest-but-Curious”. As a result, the compromise of such server results into entire system vulnerability. To address this, we propose a novel P rivacy P reserving (PP) multi-instance iris remote authentication system to accord with attacks at the malicious server and over the transmission channel. Our scheme uses F ully H omomorphic E ncryption (FHE) to achieve the confidentiality of the fused iris templates and polynomial factorization algorithm to achieve the integrity of the matching result. We propose a PP iris authentication system using P rivate M ulti-C lass P erceptron (PMCP) by using the properties of FHE. Moreover, we propose C ontradistinguish S imilarity A nalysis (CSA), a feature level fusion technique that minimizes the between-class correlations and maximizes the pair-wise correlations. Our method has experimented on IITD and CASIA-V3-Interval iris databases to check the effectiveness and robustness. Experimental results show that our method provides improved accuracy, and eliminates the need to trust the cloud server when compared to the state-of-the-art approaches.

The m-healthcare system can benefit medical users by providing high-quality pervasive healthcare monitoring, the growing of m-healthcare system is still strange on how we fully understand and manage the challenges facing in this m-healthcare system, especially during a medical emergency. In this paper, we propose a new secure and privacy-preserving opportunistic computing framework, called SPOC, to address this challenge. With the help of our proposed SPOC framework, each medical user who is in an emergency can achieve the user-centric privacy access control to allow only those qualified helpers to participate in the opportunistic computing to balance the high reliability of PHI process and minimizing PHI privacy disclosure in m-Health care emergency. We introduce an efficient user-centric privacy access control in SPOC framework, which is based on an attribute-based access control and a new privacy-preserving scalar product computation (PPSPC) technique, and allows a medical user to decide who can participate in the opportunistic computing to assist in processing his great PHI data.

Cancellable fingerprint templates effectively protect original fingerprint data by revoking an accorded template and reissuing a new template. Alignment-free cancellable templates require no image pre-alignment and therefore does not go through from inaccurate singular point detection. In our proposed method, we focused on generating a cancellable template which is alignment-free. The template is generated by the building of R rectangles by varying the directions over every minutia succeeded by the computation of translation invariant and rotation invariant adjoining relation. The computed feature set is quantized & mapped into a cube to produce a binary string. Further, we apply modulo operation on the generated bit string to get reduced bit string which mitigates the risk of the ARM (Attack via Record Multiplicity). Later, we apply Discrete Fourier Transform (DFT) to convert reduced binary string into a complex vector. The result is then multiplied by an arbitrary matrix to produce the cancellable template. We evaluated proposed scheme on databases FVC 2004 DB1-DB3 & FVC 2002 DB1-DB3 and results fulfills the conditions of Biometric Template Protection Scheme(BTPS) and it gives competitive performance(in terms of EER) when compared to existing methods.

Biometric authentication systems gained a huge public attention when compared to password authentication systems due to its direct connection with user identity. As Biometrics cannot be revoked or cancelled, several security and privacy issues will come if biometric templates are directly store into the database. To overcome this, there is a need to protect the biometric templates by applying transformations and at the same time accuracy won’t be compromised. The transformed template is called cancellable template and it must satisfies all the requirements of Biometric Template Protection Schemes (BTPS) i.e., Diversity, unlinkability, accuracy, noninvertibility. The proposed method focused on generating cancellable iris templates by using discrete logarithm. By applying 1-D log Gabor filter on the iris images, iris codes were generated. Later a row vector is formed by appending next row to the previous one. Then the row vector is partitioned and converted into decimal vector. To achieve security or noninvertibility decimal vector is subjected to discrete logarithm over a prime field. To confirm the accuracy of the proposed approach, experiments are performed on CASIA-V 1.0 & CASIA-V3-Interval and achieved EER as 0.57% & 0.79%. Although the EER seems somewhat high, proposed approach is efficient in terms of security and noninvertible perspectives.