In this paper, we define and explore proofs of retrievability (PORs). A POR scheme enables an archive or back-up service (prover) to produce a concise proof that a user (verifier) can retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient for the user to recover F in its entirety.A POR may be viewed as a kind of cryptographic proof of knowledge (POK), but one specially designed to handle a large file (or bitstring) F. We explore POR protocols here in which the communication costs, number of memory accesses for the prover, and storage requirements of the user (verifier) are small parameters essentially independent of the length of F. In addition to proposing new, practical POR constructions, we explore implementation considerations and optimizations that bear on previously explored, related schemes.In a POR, unlike a POK, neither the prover nor the verifier need actually have knowledge of F. PORs give rise to a new and unusual security definition whose formulation is another contribution of our work.We view PORs as an important tool for semi-trusted online archives. Existing cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It is also natural, however, for users to want to verify that archives do not delete or modify files prior to retrieval. The goal of a POR is to accomplish these checks without users having to download the files themselves. A POR can also provide quality-of-service guarantees, i.e., show that a file is retrievable within a certain time bound.

PORs: Proofs of Retrievability for Large Files

The early prediction of deterioration could have an important role in supporting healthcare professionals, as an estimated 11% of deaths in hospital follow a failure to promptly recognize and treat deteriorating patients1. To achieve this goal requires predictions of patient risk that are continuously updated and accurate, and delivered at an individual level with sufficient context and enough time to act. Here we develop a deep learning approach for the continuous risk prediction of future deterioration in patients, building on recent work that models adverse events from electronic health records2–17 and using acute kidney injury—a common and potentially life-threatening condition18—as an exemplar. Our model was developed on a large, longitudinal dataset of electronic health records that cover diverse clinical environments, comprising 703,782 adult patients across 172 inpatient and 1,062 outpatient sites. Our model predicts 55.8% of all inpatient episodes of acute kidney injury, and 90.2% of all acute kidney injuries that required subsequent administration of dialysis, with a lead time of up to 48 h and a ratio of 2 false alerts for every true alert. In addition to predicting future acute kidney injury, our model provides confidence assessments and a list of the clinical features that are most salient to each prediction, alongside predicted future trajectories for clinically relevant blood tests9. Although the recognition and prompt treatment of acute kidney injury is known to be challenging, our approach may offer opportunities for identifying patients at risk within a time window that enables early treatment. A deep learning approach that predicts the risk of acute kidney injury may help to identify patients at risk of health deterioration within a time window that enables early treatment.

A clinically applicable approach to continuous prediction of future acute kidney injury

Vehicular ad hoc networks (VANETs) are becoming the most promising research topic in intelligent transportation systems, because they provide information to deliver comfort and safety to both drivers and passengers. However, unique characteristics of VANETs make security, privacy, and trust management challenging issues in VANETs’ design. This survey article starts with the necessary background of VANETs, followed by a brief treatment of main security services, which have been well studied in other fields. We then focus on an in-depth review of anonymous authentication schemes implemented by five pseudonymity mechanisms. Because of the predictable dynamics of vehicles, anonymity is necessary but not sufficient to thwart tracking an attack that aims at the drivers’ location profiles. Thus, several location privacy protection mechanisms based on pseudonymity are elaborated to further protect the vehicles’ privacy and guarantee the quality of location-based services simultaneously. We also give a comprehensive analysis on various trust management models in VANETs. Finally, considering that current and near-future applications in VANETs are evaluated by simulation, we give a much-needed update on the latest mobility and network simulators as well as the integrated simulation platforms. In sum, this paper is carefully positioned to avoid overlap with existing surveys by filling the gaps and reporting the latest advances in VANETs while keeping it self-explained.

A Survey on Recent Advances in Vehicular Network Security, Trust, and Privacy

Multi-key privacy-preserving deep learning in cloud computing

An Efficient Algorithm for Media-based Surveillance System (EAMSuS) in IoT Smart City Framework

Cloud computing is an evolving paradigm that has been resulted as an adoption of available technologies. Although cloud technology allows users to take more benefits from available infrastructures, and virtualization, which is an enabling technology provided by the cloud allows users to manage and use the resources in an efficient and easiest manner, it does not guarantee data integrity and security over the resources stored in the cloud. Though many security frameworks have been developed for the cloud, still there may be loss of data or loss of control over data uploaded into the cloud. And also, many solutions for data integrity by third-party auditor are available but they are semi-trustable, if third-party auditors (TPA) compromise unauthorized access over the resource in the cloud. Hence, our proposed scheme focuses on extended framework that guarantees data integrity by involving data owner to perform auditing on the outsourced data in the cloud. And so, our proposed scheme achieves data integrity and guarantees security to the data owners for their resource in the cloud to major extent. Therefore, this type of TPA approach creates awareness to the data owner of their resource and thereby guarantees data integrity for every resources stored in the cloud.

Enhanced Security Framework for Data Integrity Using Third-party Auditing in the Cloud System

The Internet of things (IoT) enables global connectivity over the worldwide physical objects. The world is made smart with the development of science and technology, by people collaborating automatically with heterogeneous devices. The exchange of massive information between the devices gives rise to enormous energy requirements. The IoT is interconnected to big data analytics and cloud computing to predict the behavior of smart devices, provide useful business insights, and act as a feedback control. In addition, most of the organizations would adapt to the growing interest of smart world which in turn energy demand increases more rapidly. Moreover, there is a never-ending increase in number of vendors and users of various technologies. Green IoT prominently focuses on reducing the environmental problems and creating a sustainable environment related to IoT. It has been emphasized that the utilization of energy-efficient technologies in IoT either reduces the impact of greenhouse gases or inhibit greenhouse effect in various IoT applications. Green IoT assists in maintaining the climatic condition by introducing low energy consumption devices or electrical appliances, minimizing greenhouse gas emissions, utilizing carbon-free materials, and promoting reusability. In order to achieve sustainable environment, an outline of Green IoT and its applications are described in the perspective of minimizing the energy utilization in IoT. The designing of green data centers, sensor network, and cloud computing is discussed with certain policies in order to save energy. In addition, the different Green IoT strategies are analyzed and the principles that can be adapted to attain Green IoT also discussed. Finally, various applications of IoT are described such as smart home which allows the user to control the smart devices like automated doors, smart locks, security cameras, electrical appliances with the application on a smart phone or any other connected devices. Smart e-health is useful in monitoring and tracking patients, and smart city assists people by interconnecting systems, thereby offers required services in order to provide a smarter view for the present scenario and which in turn makes the IoT greener.

Application of IoT in Green Computing

Data is gathered from millions of patients across various healthcare organizations could be framed as electronic health record (EHR). It comprises of data in various forms that include laboratory results, diagnosis reports, medical images, demographic information about patient, and clinical notes. Transformation in the health care could be done with the help of deep learning techniques since these techniques outperform than the conventional machine learning methods. In addition, a huge and complex dataset gets generated day by day, and thus, it enables the need for the deployment of deep learning models. It is examined that the clinical decision in certain fields that vary from analysis of medical research issues and to suggest as well as prioritize the treatments in order to detect abnormalities, and in addition, the identification of genomic markers in tissue samples is done. Deep learning is applied in EHR system to derive patient representation to provide enhanced clinical predictions, and augments clinical decision systems. Moreover, it is applied in order to detect the disease in the earlier stages, determining the clinical risk, forecasting the future need of regular checkups and in addition prediction of hospitalization in the near future if required. According to statistical report, the deep learning software market is estimated to reach 180 million US dollars in size by 2020. The availability of a huge amount of clinical information particularly EHR has stimulated the growth of deep learning techniques which assist in rapid analysis of patient’s data. Nowadays, EHR is being incorporated with deep learning techniques and tools into EHR systems which provide deep insights for health outcomes. The market for EHR deep learning tools is predicted to exceed $34 billion by the mid of 2020s, motivated massively by an emerging desire to automate tasks and provide deeper insights into clinical issues. The global health IT market is anticipated to be worth a surprising $223.16 billion by 2023, driven in part by deep learning. EHR is a thriving research domain for deep learning researchers since it helps to achieve higher accuracies in medical conditions. Clinical information could be extracted by applying deep learning techniques. The extraction is of various types, namely retrieval of single idea, extracting the association, time-based extraction, and advancement of abbreviation. In EHR representation learning process, concept and patient representations are included to obtain detailed analysis and decisive predictive functionalities. Outcome prediction in deep EHR is categorized as static and temporal prediction to predict patient outcomes. The clinical deep learning is more interpretable under the category of maximum activation, constraints, qualitative clustering, and mimic learning in EHR analysis. The objective of this chapter is to provide an insight into the digital transformation of EHR through deep learning techniques. It discusses the deep learning framework and challenges that occur during the development of deep learning models for EHR. Moreover, it also focuses on the deep learning prediction techniques for various diseases and recent advancements on deep learning techniques in the aspect of providing precise medicine and future generation health care.

Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Cloud computing empowers sharing of computing resources dynamically to a wide range of end users. The workload on the cloud resources is amassed immensely with the advancement of new applications. To increase the utilisation of the virtual machine in the cloud data center, an efficient load balancing technique is predominantly crucial. Load balancing is the heart of the data centre in cloud environment which makes all the virtual machines accomplish the same amount of workload and helps the virtual machines deliver the services with minimal time delay. To ensure optimum resource usage and fast processing time, M/G/1 is developed with vacation queuing model (VQM-LB) for adjusting the workload of the under-loaded virtual machine in a cloud environment. The proposed system uses vacation and threshold policy to efficiently control the workload level of each virtual machine in the data center , and reduces the energy consumption and cost accordingly. The result obtained in our proposed model assuredly bounces optimum solutions and apparently indicates the triumph of an efficient load balancing of tasks on minimal energy consumption and cost.

http://www.inass.org/share/2016123125.pdf

Threshold-Based Workload Control for an Under-Utilized Virtual Machine in Cloud Computing

Mobile ad hoc network (MANET) is a self-organised network that allows communication between the nodes that are in radio range of each other. MANET nodes can join and leave the network dynamically, leading to a rapid change in network topology. As not all nodes in MANET are in radio range of each other, the intermediate nodes help in routing of the packets. The packets are transferred from the source node to the destination node over the network. As the nodes in MANET are battery powered, the intermediate nodes may at times lose its power completely which might lead to partitioning of the network. Hence, the communication between the nodes might get disconnected and thus the lifetime of the network gets reduced. Thus, the limited power resource is one of the major disadvantages here, as the batteries cannot be recharged or replaced in many of the locations where MANETs are deployed. This work is aimed at providing a solution mechanism that efficiently utilises the existing power resources in MANET nodes thereby extending the lifetime of the network.

Balamurugan Balusamy

Papers

Enhanced Security Framework for Data Integrity Using Third-party Auditing in the Cloud System

Application of IoT in Green Computing

Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Threshold-Based Workload Control for an Under-Utilized Virtual Machine in Cloud Computing

Enhancing network lifetime through power-aware routing in MANET