Identity-based controlled delegated outsourcing data integrity auditing scheme

With the continuous development of cloud computing, the application of cloud storage has become more and more popular. To ensure the integrity and availability of cloud data, scholars have proposed several cloud data auditing schemes. Still, most need help with outsourced data integrity, controlled outsourcing, and source file auditing. Therefore, we propose a controlled delegation outsourcing data integrity auditing scheme based on the identity-based encryption model. Our proposed scheme allows users to specify a dedicated agent to assist in uploading data to the cloud. These authorized proxies use recognizable identities for authentication and authorization, thus avoiding the need for cumbersome certificate management in a secure distributed computing system. While solving the above problems, our scheme adopts a bucket-based red-black tree structure to efficiently realize the dynamic updating of data, which can complete the updating of data and rebalancing of structural updates constantly and realize the high efficiency of data operations. We define the security model of the scheme in detail and prove the scheme's security under the difficult problem assumption. In the performance analysis section, the proposed scheme is analyzed experimentally in comparison with other schemes, and the results show that the proposed scheme is efficient and secure.

Determining factors affecting Filipino consumers' behavioral intention to use cloud storage services: An extended technology acceptance model integrating valence framework

Cloud Storage (CS) is a service that digitally stores, remotely manages, backs up, and renders internet-accessible data. However, despite its known benefits compared to traditional storage devices, this service is not widely used in developing nations such as the Philippines. This study integrated the Valence Theoretical Framework into the Extended Technology Acceptance Model (ETAM) to evaluate the influence of twelve variables on Filipino consumers' behavioral intention (BI) toward adopting CS services. The data is gathered through an online survey. Structural Equation Modeling was employed to examine the responses of 431 cloud users, mainly students and working professionals. Results showed that Perceived Benefit and Perceived Usefulness were the strongest determinants of BI. The Job Relevance was also found to be a significant factor. Therefore, CS providers should find additional ways to make their offerings more beneficial for the daily tasks of students and working individuals. Furthermore, considering the substantial influence of Perceived Risk and Subjective Norms on BI, CS providers must strengthen their security measures to boost users' trust in their services. Consumers who receive excellent service are likely to give positive reviews, which can be helpful to individuals who might also be considering purchasing CS for their data. Although the focus of this study is CS services, this can also serve as a reference when analyzing the BI of consumers concerning the adoption of other novel technologies applied in various sectors, including education, e-commerce, healthcare, and more.

Patient-centered radiology: a roadmap for outpatient imaging

Creating a patient-centered experience is becoming increasingly important for radiology departments around the world. The goal of patient-centered radiology is to ensure that radiology services are sensitive to patients' needs and desires. This article provides a framework for addressing the patient's experience by dividing their imaging journey into three distinct time periods: pre-exam, day of exam, and post-exam. Each time period has aspects that can contribute to patient anxiety. Although there are components of the patient journey that are common in all regions of the world, there are also unique features that vary by location. This paper highlights innovative solutions from different parts of the world that have been introduced in each of these time periods to create a more patient-centered experience. CLINICAL RELEVANCE STATEMENT: Adopting innovative solutions that help patients understand their imaging journey and decrease their anxiety about undergoing an imaging examination are important steps in creating a patient centered imaging experience. KEY POINTS: • Patients often experience anxiety during their imaging journey and decreasing this anxiety is an important component of patient centered imaging. • The patient imaging journey can be divided into three distinct time periods: pre-exam, day of exam, and post-exam. • Although components of the imaging journey are common, there are local differences in different regions of the world that need to be considered when constructing a patient centered experience.

Mobile application using DCDM and cloud-based automatic plant disease detection

Farming has a plethora of difficult responsibilities, and plant monitoring is one of them. There is also an urgent need to increase the number of alternative techniques for detecting plant diseases, which is now lacking. The agriculture and agricultural support sectors in India provide employment for the great majority of the country's people. In India, the agricultural production of the country is directly connected to the country's economic growth rate. In order to sustain healthy plant development, a variety of processes must be followed, including consideration of environmental factors and water supply management for the optimal production of crops. It is inefficient and uncertain in its outcomes to use the traditional method of watering a lawn. The devastation of more than 18% of the world's agricultural produce is caused by disease attacks on an annual basis. Because it is difficult to execute these activities manually, identifying plant diseases is essential to decreasing losses in the agricultural product business. In addition to diagnosing a wide range of plant ailments, our method also includes the identification of infections as a prophylactic step. Below is a detailed description of a farm-based module that includes numerous cloud data centers and data conversion devices for accurately monitoring and managing farm information and environmental elements. This procedure involves imaging the plant's visually obvious signs in order to identify disease. It is recommended that the therapy be used in conjunction with an application to minimize any harm. Increased productivity as a result of the suggested approach would help both the agricultural and irrigation sectors. The plant area module is fitted with a mobile camera that captures images of all of the plants in the area, and all of the plants' information is saved in a database, which is accessible from any computer with Internet access. It is planned to record information on the plant's name, the type of illness that has been afflicted, and an image of the plant. In a wide range of applications, bots are used to collect images of various plants as well as to prevent disease transmission. To ensure that all information given is retained on the Internet, data is collected and stored in cloud storage as it becomes essential to regulate the condition. According to our findings from our research on wide images of healthy and ill fruit and plant leaves, real-time diagnosis of plant leaf diseases may be done with 98.78% accuracy in a laboratory environment. We utilized 40,000 photographs and then analyzed 10,000 photos to construct a DCDM deep learning model, which was then used to train additional models on the data set. Using a cloud-based image diagnostic and classification service, consumers may receive information about their condition in less than a second on average, with the process requiring only 0.349 s on average.

Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science

A common task in scientific computing is the data reduction. This workflow extracts the most important information from large input data and stores it in smaller derived data objects. The derived data objects can then be used for further analysis. Typically, these workflows use distributed storage and computing resources. A straightforward setup of storage media would be low-cost tape storage and higher-cost disk storage. The large, infrequently accessed input data are stored on tape storage. The smaller, frequently accessed derived data is stored on disk storage. In a best-case scenario, the large input data is only accessed very infrequently and in a well-planned pattern. However, practice shows that often the data has to be processed continuously and unpredictably. This can significantly reduce tape storage performance. A common approach to counter this is storing copies of the large input data on disk storage. This contribution evaluates an approach that uses cloud storage resources to serve as a flexible cache or buffer, depending on the computational workflow. The proposed model is explored for the case of continuously processed data. For the evaluation, a simulation tool was developed, which can be used to analyse models related to storage and network resources. We show that using commercial cloud storage can reduce on-premises disk storage requirements, while maintaining an equal throughput of jobs. Moreover, the key metrics of the model are discussed, and an approach is described, which uses the simulation to assist with the decision process of using commercial cloud storage. The goal is to investigate approaches and propose new evaluation methods to overcome future data challenges.

A Survey on Security and Privacy Issues in Contact Tracing Application of Covid-19

In response to the coronavirus (COVID-19) pandemic, Government and public health authorities around the world are developing contact tracing apps as a way to trace and slow the unfold of the virus. There is major divergence among nations, however, between a "privacy-first" approach that protects citizens' information at the price of very restricted access for public health authorities and a "data-first" approach that stores massive amounts of knowledge that, whereas of immeasurable price to epidemiologists. Contact tracing apps work by gathering information from people who have tested positive for the virus and so locating and notifying individuals with whom those people are in shut contact, oftentimes by use of GPS, Bluetooth, or wireless technology. All of the user's information is employed and picked up, the study found that users' information would be created anonymous, encrypted, secured, and can be transmitted on-line and stored solely in an aggregated format. Contact tracing apps use either a centralized or a decentralized approach to work the user's information. Apps that use a centralized approach have high privacy risks. In this paper, the researcher's contributions related to the security and privacy of Contact tracing apps have been discussed and, later research gaps have been identified with proposed solutions.

Attribute based honey encryption algorithm for securing big data: Hadoop distributed file system perspective

Hadoop has become a promising platform to reliably process and store big data. It provides flexible and low cost services to huge data through Hadoop Distributed File System (HDFS) storage. Unfortunately, absence of any inherent security mechanism in Hadoop increases the possibility of malicious attacks on the data processed or stored through Hadoop. In this scenario, securing the data stored in HDFS becomes a challenging task. Hence, researchers and practitioners have intensified their efforts in working on mechanisms that would protect user's information collated in HDFS. This has led to the development of numerous encryption-decryption algorithms but their performance decreases as the file size increases. In the present study, the authors have enlisted a methodology to solve the issue of data security in Hadoop storage. The authors have integrated Attribute Based Encryption with the honey encryption on Hadoop, i.e., Attribute Based Honey Encryption (ABHE). This approach works on files that are encoded inside the HDFS and decoded inside the Mapper. In addition, the authors have evaluated the proposed ABHE algorithm by performing encryption-decryption on different sizes of files and have compared the same with existing ones including AES and AES with OTP algorithms. The ABHE algorithm shows considerable improvement in performance during the encryption-decryption of files.

Internet of Things based real-time electric vehicle load forecasting and charging station recommendation

Electric vehicles (EVs) are emerging as a favorable strategy to meet the increasing environmental concerns and energy insufficiency, and this trend is expected to grow in the near future. However, the inadequate charging infrastructure is becoming a major barrier to the wide acceptance of EVs. Deployment of this infrastructure is expected to maximize the adoption of EVs to facilitate users' range anxiety. Therefore, connectivity between the charging stations (CS) is mandatory. Understanding the real-time status of CSs can provide valuable information to users such as availability of charging provisions, reserves and the time to reach the CS. The intent of this paper is to provide a better EV charging system by utilizing the advantages of the Internet of Things (IoT) technology. The IoT paradigm offers the present facilities a real-time interactional view of the physical world by a variety of sensors and broadcasting tools. This research article proposes a real-time server-based forecasting application: i) to provide scheduling management to avoid waiting time; and ii) to provide a real-time CS recommendation for EVs with an economic cost and reduced charging time. In addition, the proposed scheme avoids third-party intervention and protects EV user privacy and complex information exchange between the user and CS. The end users can easily use the CS based on their requirements. This synergetic application is built up through the PHP programming language in the Linux UBUNTU 16.04 LTS operating system, and all relevant information is processed and managed through Cloud Structured Query Language (CSQL) from a Google cloud platform. The effectiveness of this application is also validated through a low-cost test system using LTC 4150, ESP 8266 Wi-Fi module and Arduino.

Usage of cloud storage facilities by medical students in a low-middle income country, Sri Lanka: a cross sectional study

Background

Cloud storage facilities (CSF) has become popular among the internet users. There is limited data on CSF usage among university students in low middle-income countries including Sri Lanka. In this study we present the CSF usage among medical students at the Faculty of Medicine, University of Kelaniya.

Methods

We undertook a cross sectional study at the Faculty of Medicine, University of Kelaniya, Sri Lanka. Stratified random sampling was used to recruit students representing all the batches. A self-administrated questionnaire was given.

Results

Of 261 (90.9%) respondents, 181 (69.3%) were females. CSF awareness was 56.5% (95%CI: 50.3–62.6%) and CSF usage was 50.8% (95%CI: 44.4–57.2%). Awareness was higher in males (P = 0.003) and was low in senior students. Of CSF aware students, 85% knew about Google Drive and 70.6% used it. 73.6 and 42.1% knew about Dropbox and OneDrive. 50.0 and 22.0% used them respectively. There was no association between CSF awareness and pre-university entrance or undergraduate examination performance. Inadequate knowledge, time, accessibility, security and privacy concerns limited CSF usage. 69.8% indicated that they would like to undergo training on CSF as an effective tool for education.

Conclusion

CSF awareness and usage among the students were 56.5 and 50.8%. Google drive is the most popular CSF. Lack of knowledge, accessibility, concerns on security and privacy limited CSF usage among students. Majority were interested to undergo training on CSF and undergraduate Information Communication Technology (ICT) curricula should introduce CSF as effective educational tools.

Secure Cloud-Based EHR System Using Attribute-Based Cryptosystem and Blockchain

To achieve confidentiality, authentication, integrity of medical data, and support fine-grained access control, we propose a secure electronic health record (EHR) system based on attribute-based cryptosystem and blockchain technology. In our system, we use attribute-based encryption (ABE) and identity-based encryption (IBE) to encrypt medical data, and use identity-based signature (IBS) to implement digital signatures. To achieve different functions of ABE, IBE and IBS in one cryptosystem, we introduce a new cryptographic primitive, called combined attribute-based/identity-based encryption and signature (C-AB/IB-ES). This greatly facilitates the management of the system, and does not need to introduce different cryptographic systems for different security requirements. In addition, we use blockchain techniques to ensure the integrity and traceability of medical data. Finally, we give a demonstrating application for medical insurance scene.

Monitoring sound and light continuously in an intensive care unit patient room: A pilot study

PURPOSE

To determine the feasibility of continuous recording of sound and light in the intensive care unit (ICU).

MATERIALS AND METHODS

Four 1-hour baseline scenarios in an empty ICU patient room by day and night (doors open or closed and maximal or minimal lighting) and two daytime scenarios simulating a stable and unstable patient (quiet or loud devices and staff) were conducted. Sound and light levels were continuously recorded using a commercially available multisensor monitor and transmitted via the hospital's network to a cloud-based data storage and management system.

RESULTS

The empty ICU room was loud with similar mean sound levels of 45 to 46 dBA for the day and night simulations. Mean levels for maximal lighting during day and night ranged from 1306 to 1812 lux and mean levels for minimum lighting were 1 to 3 lux. The mean sound levels for the stable and unstable patient simulations were 61 and 81 dBA, respectively. The mean light levels were 349 lux for the stable patient and 1947 lux for the unstable patient.

CONCLUSIONS

Combined sound and light can be continuously and easily monitored in the ICU setting. Incorporating sound and light monitors in ICU rooms may promote an enhanced patient- and staff-centered healing environment.

Load balancing prediction method of cloud storage based on analytic hierarchy process and hybrid hierarchical genetic algorithm

With the continuous expansion of the cloud computing platform scale and rapid growth of users and applications, how to efficiently use system resources to improve the overall performance of cloud computing has become a crucial issue. To address this issue, this paper proposes a method that uses an analytic hierarchy process group decision (AHPGD) to evaluate the load state of server nodes. Training was carried out by using a hybrid hierarchical genetic algorithm (HHGA) for optimizing a radial basis function neural network (RBFNN). The AHPGD makes the aggregative indicator of virtual machines in cloud, and become input parameters of predicted RBFNN. Also, this paper proposes a new dynamic load balancing scheduling algorithm combined with a weighted round-robin algorithm, which uses the predictive periodical load value of nodes based on AHPPGD and RBFNN optimized by HHGA, then calculates the corresponding weight values of nodes and makes constant updates. Meanwhile, it keeps the advantages and avoids the shortcomings of static weighted round-robin algorithm.

An Efficient Searchable Encryption Against Keyword Guessing Attacks for Sharable Electronic Medical Records in Cloud-based System

Preserving the privacy of electronic medical records (EMRs) is extremely important especially when medical systems adopt cloud services to store patients' electronic medical records. Considering both the privacy and the utilization of EMRs, some medical systems apply searchable encryption to encrypt EMRs and enable authorized users to search over these encrypted records. Since individuals would like to share their EMRs with multiple persons, how to design an efficient searchable encryption for sharable EMRs is still a very challenge work. In this paper, we propose a cost-efficient secure channel free searchable encryption (SCF-PEKS) scheme for sharable EMRs. Comparing with existing SCF-PEKS solutions, our scheme reduces the storage overhead and achieves better computation performance. Moreover, our scheme can guard against keyword guessing attack, which is neglected by most of the existing schemes. Finally, we implement both our scheme and a latest medical-based scheme to evaluate the performance. The evaluation results show that our scheme performs much better performance than the latest one for sharable EMRs.