Earticle

In a cloud computing environment, frequent data interactions require high information security. This paper briefly overviews encrypted data storage in the cloud computing environment. A dual encryption scheme that combined identity attributes and security devices was proposed. The scheme was evaluated through simulation experiments conducted on a laboratory server. The results indicated that an increase in the size of the storage file led to an increase in the time overhead of the encryption scheme. Furthermore, the dual encryption scheme exhibited slightly higher time overhead than a single security device encryption scheme, but was similar to a single identity attribute-based encryption scheme. For cloud storage data, the successful queries of encrypted data and effective resistance against third-party attacks were only achieved when the number of overlapping identity attributes was 5, which was the same as the storage user.

Aerial Computing, utilizing unmanned aerial vehicles (UAVs), has emerged as a promising solution to enhance mobile cloud computing (MCC) infrastructure for the Internet of Things (IoT). The continuous generation of vast amounts of data by IoT devices requires efficient processing and monitoring for timely decision-making. However, wireless connections between IoT devices and remote servers can be unreliable, resulting in data loss. UAVs, with their increasing processing power and autonomy, can act as bridges between IoT devices and remote servers such as edge or cloud computing. In that context, this paper proposes a continuous time Markov chain (CTMC) models for an aerial computing system to evaluate system dependability metrics including availability and reliability. Sensitivity analysis is conducted to provide extended CTMC models with improved system availability. The proposed advanced model reduces downtime by 62 h compared to the baseline model, showcasing the potential of UAVs in enhancing the availability and reliability of MCC infrastructures. The use of UAVs and MCC in aerial computing is believed to be a win?win solution for cost-effective and energy-saving communication and computation services in various environments.

Throughout the world, aging populations and doctor shortages have helped drive the increasing demand for smart healthcare systems. Recently, these systems have benefited from the evolution of the Internet of Things (IoT), big data, and machine learning. However, these advances result in the generation of large amounts of data, making healthcare data analysis a major issue. These data have a number of complex properties such as high-dimensionality, irregularity, and sparsity, which makes efficient processing difficult to implement. These challenges are met by big data analytics. In this paper, we propose an innovative analytic framework for big healthcare data that are collected either from IoT wearable devices or from archived patient medical images. The proposed method would efficiently address the data heterogeneity problem using middleware between heterogeneous data sources and MapReduce Hadoop clusters. Furthermore, the proposed framework enables the use of both fog computing and cloud platforms to handle the problems faced through online and offline data processing, data storage, and data classification. Additionally, it guarantees robust and secure knowledge of patient medical data.

With the rapid growth of genomic data, new requirements have emerged that are difficult to handle with big data storage and analysis techniques. Regardless of the size of an organization performing genomic data analysis, it is becoming increasingly difficult for an institution to build a computing environment for storing and analyzing genomic data. Recently, cloud computing has emerged as a computing environment that meets these new requirements. In this paper, we analyze and compare existing distributed and parallel NGS (Next Generation Sequencing) analysis based on cloud computing environment for future research.

The cloud environment makes it possible to analyze large data sets in a scalable computing infrastructure. In the bioinformatics field, the applications are composed of the complex workflow tasks, which require huge data storage as well as a computing-intensive parallel workload. Many approaches have been introduced in distributed solutions. However, they focus on static resource provisioning with a batch-processing scheme in a local computing farm and data storage. In the case of a large-scale workflow system, it is inevitable and valuable to outsource the entire or a part of their tasks to public clouds for reducing resource costs. The problems, however, occurred at the transfer time for huge dataset as well as there being an unbalanced completion time of different problem sizes. In this paper, we propose an adaptive resource-provisioning scheme that includes run-time data distribution and collection services for hiding the data transfer time. The proposed adaptive resource-provisioning scheme optimizes the allocation ratio of computing elements to the different datasets in order to minimize the total makespan under resource constraints. We conducted the experiments with a well-known sequence alignment algorithm and the results showed that the proposed scheme is efficient for the cloud environment.

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.

In this paper, 10 years of airline data set in USA is collected. And, the analysis of the airline data set is performed using cloud computing service of Microsoft Azure which runs Hadoop cluster in the cloud. Hadoop Hive QL statements have been used for analyzing the data. Data visualization has been achieved by extracting the output of the Hive code. Using big data technologies like Hadoop and Hive in the cloud, it is easy to analyze the massive data set in SQL like language, export the output results and visualize them excel spreadsheets. Interesting sets of trends and patterns exists in this large data sets, the paper presents the insights that resides between flight diversions and flight distance, flight cancellation and flight distance for the time period.

The purpose of this paper is to suggest the development of emergency preparedness policy to implement intelligent government in the 4th Industrial Revolution. For accomplishing the research goal, I examined the significance of emergency preparedness policy in the 4th Industrial Revolution and the emergency preparedness policy of intelligent government in connection with the use of artificial intelligence(AI), big data analysis, cloud computing, and drone. And the development suggestions of emergency preparedness policy in the intelligent government are presented as follows. First, it is necessary to construct a crisis management decision support system using artificial intelligence, big data analysis, and IoT, etc. Second, we have to establish a system and technology for linking and utilizing private and public data. Third, it is desirable to improve the system to utilize big data in the situation of not deleting personal information or invading privacy. Fourth, big data analysis and artificial intelligence should be used to support military operations in the emergency preparedness plan, maintain government functions, and stabilize people's lives. Fifth, we need to combine AI and big data analysis to integrate civil defense resources and emergency preparedness resources, which have been classified as a national crisis and emergency management resource. Sixth, a comprehensive and trans-disciplinary crisisonomy study should be carried out based on the convergence academic system of the government's emergency preparedness policy. Seventh, it is desirable to activate educational opportunities and personnel exchanges with universities for emergency preparedness in intelligent governments to enhance the professionalism and creativity of public officials in charge. Eighth, it is necessary to utilize the expertise of military retirement officials in emergency preparedness in intelligent governments.

소셜 네트워크는 이용자들의 공통적 관심 분야를 기반으로 서로 연결된 개별적 온라인 서비스 구조이다. 소셜 네트워킹 웹사이트의 사용은 온라인 커뮤니케이션과 데이터 공유 및 상호작용 방법을 혁신적으로 바꿔놓았다. 소셜 네트워킹 사이트의 사용량이 증가함에 따라 막대한 양의 사용자 상호작용 데이터가 매일 축적되고 있어 다양한 분야에서의 적용을 위한 대량의 소셜 네트워크 데이터 마이닝 및 분석 방안이 갈수록 주목받고 있다. OLAP데이터 분석은 효과적으로 소셜 데이터를 분석하기 위하여 사용될 수 있는 유용한 데이터 분석방법이지만 계속적으로 가변하는 대량 데이터와 OLAP쿼리의 복잡성 때문에 작업 시간이 오래걸리는 문제점이 있다. 본 논문은 대량의 소셜 네트워크 데이터 분석에 적합한 클라우드 컴퓨팅의 발전이 이루어지면서 효율적이고 빠른 데이터 분석을 위하여 클라우드 컴퓨팅 플랫폼을 사용하는 OLAP기반 소셜 데이터 분석도구에 대해 설명한다.

A social network is an online structure of individuals which are related to each other based on some common relationship of interest. The usage of social networking websites has literally revolutionized the methods of online communication, data sharing and interaction. As a result of the increased usage of social networking sites a massive amount of user interaction data is been generated daily. The mining and analysis of this large amount of social network data for a variety of applications is getting popularity. OLAP analysis is a famous data analysis method which can be used to effectively analyze social data. However, because of the huge amount of continuously changing data and the complexity of OLAP queries, this data analysis in a time consuming task. The advances in cloud computing motivated us to use the cost effective cloud computing infrastructure for the task of analyzing large amount of social network data. This paper presents an OLAP based social data analysis tool which uses cloud computing platform for efficient and timely analysis of data. The usage of cloud computing infrastructure results in reduction of computational cost, device and location independence, and an increase in peak-load capacity.

최근 국내･외 수자원 정책의 방향은 전통적인 이･치수 부문과 함께 삶의 질을 향상을 위해 지속가능한 물 관리에 대한필요가 강조되면서 수자원 정보의 수집, 관리 및 제공의 중요성이 증대되고 있다. 과거 수자원 정보는 제공하고자 하는 목적을이미 정하고 거기에 맞도록 데이터를 효과적으로 분석하는 기술에 초점이 맞추어져 있었다. 그러나 최근에는 정형 데이터뿐만아니라 비정형 데이터를 연계함으로써 새로운 가치를 도출할 수 있는 빅 데이터와 클라우드 컴퓨팅에 대한 관심이 부각되면서수자원 정보에도 변화를 가져오고 있다. 이에 본 논문에서는 수자원 정보 관리의 패러다임 변화에 능동적으로 대처하고, 수자원 정보의 효율적인 관리 및 이용을 위해 수자원 분야에서 빅 데이터와 클라우드 컴퓨팅의 적용 방안을 검토 및 제언하고자하였다. 국내외 수자원 정보 관리의 현황과 방향을 살펴보고, 빅 데이터의 3대 요소인 크기(Volume), 속도(Velocity), 다양성(Variety)과 함께 추가적으로 언급되고 있는 정확성(Veracity), 가치(Value)개념을 연계하였다. 그리고 클라우드 컴퓨팅을통해 증가하는 수자원 관련 빅 데이터와 수요자의 변화에 대해 신속하고 유연한 대처방안에 대하여 논의하였다. 앞으로의수자원 정보 관리는 정보의 크기(Volume), 속도(Velocity), 다양성(Variety) 등의 빅 데이터와 클라우드 컴퓨팅 적용을 통한인명과 재산의 보호 등 공공의 목적, 물 관리 및 재난의 예방과 대응에 필요한 정확한(Veracity) 정보의 생산, 그리고 다른분야와의 융합 등에 적극적으로 활용함으로써 수자원 정보의 가치(Value)를 높이는 방행으로 나아가야 한다.

In recent, the direction of water resources policy is changing from the typical plan for water use and flood control to the sustainable water resources management to improve the quality of life. This change makes the information related to water resources such as data collection, management, and supply is becoming an important concern for decision making of water resources policy. We had analyzed the structured data according to the purpose of providing information on water resources. However, the recent trend is big data and cloud computing which can create new values by linking unstructured data with structured data. Therefore, the trend for the management of water resources information is also changing. According to the paradigm change of information management, this study tried to suggest an application of big data and cloud computing in water resources field for efficient management and use of water. We examined the current state and direction of policy related to water resources information in Korea and an other country. Then we connected volume, velocity and variety which are the three basic components of big data with veracity and value which are additionally mentioned recently. And we discussed the rapid and flexible countermeasures about changes of consumer and increasing big data related to water resources via cloud computing. In the future, the management of water resources information should go to the direction which can enhance the value(Value) of water resources information by big data and cloud computing based on the amount of data(Volume), the speed of data processing(Velocity), the number of types of data(Variety). Also it should enhance the value(Value) of water resources information by the fusion of water and other areas and by the production of accurate information(Veracity) required for water management and prevention of disaster and for protection of life and property.

모바일 클라우드 컴퓨팅 시스템은 일반적으로 데이터 보호와 상호 인증을 위하여 공개키 암호화 기법을 사용하고 있는데 최근 전통적인 공개키 암호화 기술의 변형인 ID-기반 암호화(IBC)가 주목받고 있다. IBC의 증명서-무통제 접근은클라우드 환경의 동적인 성격에 더 적합하지만, 모바일 장치에 대하여 처리 오버헤드를 최소화하는 보안 프레임워크가필요하다. 본 논문에서는 모바일 클라우드 컴퓨팅에서의 계층적 ID-기반 암호화(HIBE)의 사용을 제안한다. HIBE는 사용자 인증과 개인키 생성 등의 권한을 위임하여 최상위 공개키 생성기의 업무량을 감소시킬 수 있으므로 모바일 네트워크에 적합하다. 모바일 클라우드 시스템에서 ID-기반 인증과 ID-기반 신분확인 기법을 제안하고, 또한 안전한 데이터처리를 위한 ID-기반 인증 스킴에 대하여 기술하였다. 제안된 스킴은 단방향 해쉬 함수와 XOR 연산으로 설계하여 모바일 사용자를 위한 저 계산 비용을 갖는다.

Most mobile cloud computing system use public key cryptography to provide data security and mutual authentication. A variant of traditional public key technologies called Identity-Based Cryptography(IBC) has recently received considerable attention. The certificate-free approach of IBC may well match the dynamic qualities of cloud environment. But, there is a need for a lightweight secure framework that provides security with minimum processing overhead on mobile devices. In this paper, we propose to use hierarchical ID-Based Encryption in mobile cloud computing. It is suitable for a mobile network since it can reduce the workload of root Public Key Generators by delegating the privilege of user authentication and private key generation. The Identity-Based Encryption and Identity-Based Signature are also proposed and an ID-Based Authentication scheme is presented to secure data processing. The proposed scheme is designed by one-way hash functions and XOR operations, thus has low computation costs for mobile users

보건복지부는 2016년 2월 5일에 의료법시행규칙을 개정하여, 의료기관이 클라우드를 활용하여 전자의무기록을 외부장소에서 보관・관리할 수 있도록 허용하였으며, 동 규정은 2016년 8월 6일부터 시행되었다. 그리고 의료기관이 클라우드컴퓨팅시스템을 활용할 경우에는 클라우드컴퓨팅법의 적용을 받게 된다. 물론 의료정보의 보호에 관한 규정으로는 의료법, 보건의료기본법, 정신보건법, 모자보건법 및 개인정보보법이 있으며, 정보통신체계에 관한 법률로는 클라우드컴퓨팅법 외에도 전기통신사업법, 정보통신망법, 전자정부법 및 국가정보화기본법 등이 있다. 비록 우리나라의 클라우드컴퓨팅 관련산업이 초기모습을 띄고 있지만, 관련산업의 발전을 촉진하고 이용자를 보호하며, 특히 공공부문이 클라우드컴퓨팅산업의 발전을 선도할 수 있도록 하기 위하여, 클라우드컴퓨팅법이 제정되었다. 의료법시행규칙이 발효되고 보건복지부 고시로 ‘의무기록관리보존시설기준’이 공포됨에 따라, 중소형의료기관을 중심으로 클라우드컴퓨팅시스템이 적극 활용될 것이 예상된다. 그러나, 클라우드컴퓨팅으로 개인의료정보가 집적・공유됨으로써 의료정보보호의 문제가 대두될 수 있으므로, 클라우드컴퓨팅과 관련된 의료법령과 클라우드컴퓨팅법을 일별함으로써, 그 법적 과제를 검토해보고자 한다. 한편 2018년 5월 25일이면 유럽의 개인정보보호법인 EU 일반개인정보보호규칙(General Data Protection Regulation; GDPR)이 시행될 것이며, 이미 2017년에 들어서면서 GDPR에 근거한 클라우드컴퓨팅에 관한 행동규약(Code of Conduct)이 마련되었다. 클라우드컴퓨팅의 국경을 초월하는 가상화·분산기술에 의하여 우리나라 기업이나 클라우드컴퓨팅제공자들도 동 규칙 등의 적용을 받게 될 것이다. 그러므로 본 논문에서는 클라우드컴퓨팅시스템의 도입에 따른 규제현황을 의료법령, 개인정보법령 및 클라우드컴퓨팅법령을 중심으로 검토하고, EU GDPR과 Code of Conduct를 참고하여 의료정보클라우드컴퓨팅의 규제현황, 의료정보클라우드컴퓨팅의 법률관계, 손해배상의 문제, 정보의 국외저장 등을 검토하였다.

The Ministry of Health and Welfare revised the Enforcement Rule of the Medical Service Act on February 5, 2016, allowing medical institutions to utilize the cloud to store and manage electronic medical records in an external location. This became effective on August 6, 2016. If a medical institution utilizes a cloud computing system, it will be subject to the Act on the Development of Cloud Computing and Protection of Its Users, hereinafter referred to as the Cloud Computing Act. The Medical Service Act, the Framework Act on Health and Medical Service, the Act on Mental Health and Welfare Service, the Mother and Child Health Act, and the Personal information Protection Act also regulate the protection of medical information, and, in addition to the Cloud Computing Act, the Telecommunications Business Act, the Act of Information and Communication Networks, the eGovernment Act, and the Framework Act on National Informatization serve to regulate information and communication systems. Korea's cloud computing–related industries are still in their infancy. However, to promote the cloud computing industry, the Cloud Computing Act has been introduced so that the public sector can lead. As the Enforcement Rule of the Medical Service Act is enacted and the Ministry of Health and Welfare promulgates the medical records management preservation facility standards, it is expected that cloud computing systems will be actively utilized, particularly by small and medium-size medical institutions. However, as healthcare information protection issues may arise due to the sharing of personal medical information via cloud computing, it is necessary to make the legal issues of the Medical service laws and the Cloud Computing Act clear. On May 25, 2018, the European Union's General Data Protection Regulation, hereinafter referred to as GDPR, will be enacted. The Code of Conduct for Cloud Computing, based on the GDPR, has already been established, in 2017. Because cloud computing consists of cross-border virtualization and decentralization technologies, Korean companies and cloud computing providers will also be subject to the European Privacy Protection Act. In this paper, I review the regulatory status of medical information cloud computing, the legal aspects of medical information cloud computing, the issue of compensation for damages, and the storage of information abroad, by referring to the EU GDPR and Code of Conduct.

In cloud computing environments, the database is dynamic and real-time features, and in data access it has been a Gordian knot of cloud computing research; because in the ant colony algorithm that ants finding food shares similar correlation with cloud computing node finding the access to the database. Therefore, the introduction of ant colony algorithm into cloud computing database, the introduction of the chaos function in the pheromone update, both will make the improved pheromone avoid the possibility of getting into local convergence, and hence improve the efficiency of database access in the cloud computing, reduce the load in the cloud computing. The simulation results show that the algorithm in this paper has been significantly improved in terms of cloud computing network queries and database consumption, and greatly improved the efficiency of cloud computing.

Mobile Internet make human whenever and wherever access to the Internet possibly, achieving online booking, according to the mobile phone location data, we can acquire the relevant data tourist attractions , through the real data of tourist arrivals, traffic load data, according to the computing resources of mobile Internet, and the data on the travel data mining algorithms to predict , in order to achieve tourism management ; this article aim at mobile Internet cloud computing resource allocation and data mining algorithms to predict data, the simulation results can be obtained ,the accuracy of the data is more stronger.

Cloud computing is a new generation of computing based on the layered model which provides different services to cloud customer. Conceptually, cloud computing offers a scalable platform for Big Data Analytic Applications (BDAAs) which can elastically provision resources based on data growth and complexity of analytic applications. This complexity of analysis increases with vast volumes of data in big enterprises which prefer effective and fast decision-making. Therefore, the importance of Service Level Agreement (SLA) is appeared which clarifies the roles between a customer (i.e., cloud user or Big Data analyst) and a provider for particular service provision comes. The main components of the SLAs are Quality of Service parameters which must be monitored to achieve a set of Service Level Objectives (SLOs) and detect violations. Indeed, many SLA management approaches have been developed as solutions for preventing SLA violations to avoid costly penalties. Consequently, many interesting solutions developed works of SLA violation management in cloud technology and cloud-hosted BDAAs. A survey about the existed works in terms of idea, strengths and weaknesses is introduced in this paper. Meanwhile, the challenges and new research directions in this area which require further investigation will be discussed to provide a comprehensive overview and big-picture.

Cloud computing is a new computing paradigm that attracted many computer users, business, and government agencies. Cloud computing brought a lot of advantages especially in ubiquitous services where everybody can access computer services through internet. With cloud computing, there is no need of physical hardware or servers that will support the company’s computer system, internet services and networks. One of the core services provided by cloud computing is data storage. In the past decades, data storage has been recognized as one of the main concerns of information technology. The benefits of network-based applications have led to the transition from server-attached storage to distributed storage. Based on the fact that data security is the foundation of information security, a great quantity of efforts has been made in the area of distributed storage security. In this paper, the authors tried to study the threats and attacks that possibly launch in cloud computing data storage and proposed a security mechanism.

Many Palestinian higher education institutions had a successful experience in utilizing Electronic Services and Electronic learning, such as all the academic services for student and lecturer, schedule lectures, exams schedule, viewing the academic information, grades report, printing transcript, registering courses, library services, Email services, News and Announcements, but these services are supported by web based applications or desktop application which restrict the access of the users using computers or laptops, But there is a lack in supporting these services with mobile. Especially when dealing with sensitive data as inserting grades for courses. So in this paper we present a system called Secure Mobile Cloud Computing for Sensitive Data: Teacher Services for Palestinian Higher Education Institutions (MCCTSs) which is a mobile application to facilities access using RSA algorithm to encrypt the data which sent and received through Cloud computing application. MCCTSs serve the lecturers of Palestinian higher education institutions. Agile methodology was adapted to develop MCCTSs application. MCCTSs users successfully insert the grades securely and encrypted any time anywhere using RSA algorithm. It is hoped that the result of this study will encourage the universities to engage MCCTSs in their services. From the results we obtained, it is proved that RSA provides protection for the data, which is stored in Cloud. Only authorized user can read the encrypted data and decrypt it. Even if anyone happens to read the data accidentally, the original meaning of the data will not be understood.