Cloud computing is a long-standing dream of computing as a utility, where users can store their data remotely in the cloud to enjoy on-demand services and high-quality applications from a shared pool of configurable computing resources. Thus, the privacy and security of data are of utmost importance to all of its users regardless of the nature of the data being stored. In cloud computing environments, it is especially critical because data is stored in various locations, even around the world, and users do not have any physical access to their sensitive data. Therefore, we need certain data protection techniques to protect the sensitive data that is outsourced over the cloud. In this paper, we conduct a systematic literature review (SLR) to illustrate all the data protection techniques that protect sensitive data outsourced over cloud storage. Therefore, the main objective of this research is to synthesize, classify, and identify important studies in the field of study. Accordingly, an evidence-based approach is used in this study. Preliminary results are based on answers to four research questions. Out of 493 research articles, 52 studies were selected. 52 papers use different data protection techniques, which can be divided into two main categories, namely noncryptographic techniques and cryptographic techniques. Noncryptographic techniques consist of data splitting, data anonymization, and steganographic techniques, whereas cryptographic techniques consist of encryption, searchable encryption, homomorphic encryption, and signcryption. In this work, we compare all of these techniques in terms of data protection accuracy, overhead, and operations on masked data. Finally, we discuss the future research challenges facing the implementation of these techniques.

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Access control mechanisms define the level of access to the resources among specified users. It distinguishes the users as authorized or unauthorized based on appropriate policies. Several traditional and hybrid access control models have been proposed in previous researches over the last few decades. In this study, we provide a detailed survey of access control models and compare the traditional and hybrid access control models based on their access control criteria. This survey focuses on the growing literature of access control models and summarizes it through comparative analysis, identifying limitations and illustrating the advantages of both traditional and hybrid models. This study will help the researchers to get a deep understanding of the traditional and hybrid access control models.

Cloud computing aims to provide reliable, customized, and quality of service (QoS) guaranteed dynamic computing environments for end-users. However, there are applications such as e-health and emergency response monitoring that require quick response and low latency. Delays caused by transferring data over the cloud can seriously affect the performance and reliability of real-time applications. Before outsourcing e-health care data to the cloud, the user needs to perform encryption on these sensitive data to ensure its confidentiality. Conventionally, any modification to the user data requires encrypting the entire data and calculating the hash of the data from scratch. This data modification mechanism increases communication and computation costs over the cloud. The distributed environment of fog computing is used to overcome the limitations of cloud computing. This paper proposed a certificate-based incremental proxy re-encryption scheme (CB-PReS) for e-health data sharing in fog computing. The proposed scheme improves the file modification operations, i.e., updation, deletion, and insertion. The proposed scheme is tested on the iFogSim simulator. The iFogSim simulator facilitates the development of models for fog and IoT environments, and it also measures the impact of resource management techniques regarding network congestion and latency. Experiments depict that the proposed scheme is better than the existing schemes based on expensive bilinear pairing and elliptic curve techniques. The proposed scheme shows significant improvement in key generation and file modification time.

This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

This work is aimed at investigating the potential risks linked to electroencephalography (EEG)‐based person authentication and providing solutions to mitigate these issues. Authenticating a person by EEG involves verifying the legitimacy of the signals used for user identification. EEG signals serve as a biometric modality for authentication and verification. Additional biometric modalities, such as fingerprints or irises, are vulnerable to both fabrication and degradation over time, and illicit use of dead people’s biometrics has sometimes been seen. EEG’s intrinsic properties prohibit signal imitation or postmortem acquisition, making it more reliable than other biometric modalities. This research is aimed at investigating the most recent advancements in the domain of EEG signals, clarifying the current knowledge that impacts EEG‐based authentication, and evaluating the emerging challenges. Many research publications have been collected to achieve this objective. By considering historical and recent efforts and achievements, this research also presents feasible resolutions to the emerging inquiries prompted by the ongoing advancements in EEG‐based technology. The potential future application of EEG‐based authentication has also been the subject of this scholarly discourse. A comprehensive collection of articles over the previous decade has been compiled to answer contemporary EEG signal research questions to get valuable insights. According to research findings, in February 2022, a significant milestone was achieved when the EEG signals of a deceased person were successfully captured for the first time in recorded history. However, this groundbreaking discovery may threaten EEG‐based authentication. In addition, it is found that EEG‐based authentication literature did not completely implement “liveness detection.” An updated approach for identifying liveness addresses novel challenges, that is, falsified EEG signals and a dead person’s EEG signals for EEG‐based authentication that have not been discussed in the literature. The suggested solutions put forward in this study have the potential to stimulate additional research in this area.