Big Data Security Research Articles

Preface to the First Issue

Welcome to the first issue of Artificial Intelligence and Applications (AIA) Journal. Recently, we are seeing the rapid development of Artificial Intelligence (AI) field, evidenced by the fast-growing published papers and increased practical applications. Artificial intelligence is a discipline of science and technology for making intelligent machines to simulate human abilities in learning, perception, thinking, decision, behaviour and interaction. The technology is widely used in various areas in the society, including sensing data analysis and understanding, big data analytics, security surveillance, management and planning, manufacturing, education, medical care, robotics, unmanned driving, and so on. The artificial intelligence field was found in 1956, when the term “artificial intelligence” was coined and came into the open. Since then, many theories, methods and algorithms have been developed under the main paradigms of symbolism, connectionism and behaviourism. The large boom came in the latest decade. The milestones of successful applications of deep neural networks (DNNs) in large scale image classification in 2012 and computer Go game in 2016 drew the eye of AI researchers to the deep learning approach and made AI widely known to the society. The research is extended to all aspects of intelligence science and technology, and the application of AI is extended into all scenarios of our life. The published papers are quickly increasing in existing journals and conferences, and new journals and conferences are being established. In preparing this journal, we selected the name Artificial Intelligence and Applications (AIA) because both research and application are important to the field and both are fast developing. Real-life applications of AI are enabled in recent years because the developed methodologies have led to improved performance satisfying users’ needs. On the other hand, the extensive applications also pose scientific and technical challenges which attract researchers’ attention. Such interaction between research and application will continually drive the evolution of the field. The Artificial Intelligence and Applications (AIA) is a peer-reviewed journal publishing original contributions to the theory, methods and applications of artificial intelligence. Qualified papers are the most important factor to establish the fame and impact of the journal. We solicit papers reporting novel ideas, innovative technology or applications in the AI field. We thank the authors of all submitted papers for their great efforts in supporting our journal. Let’s grow up together with the AIA Journal!

Classification of Big Data Security Based on Ontology Web Language

A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.

Comparative Analysis of Multi-digit Modular Multiplication Algorithms for public key Crypto system in Big Data Security

Big data security is a significant concern today due to rapidly growing data volume, velocity, and varieties of data in nature. Data security is a major concern because during transmission through insecure channels. From its recognition, the Montgomery multi-digit multiplication algorithms are still well-accepted approaches in the numerical calculation, chaos arithmetic, and highperformance implementation of public key cryptosystems such as RSA, Diffie-Hellman, and the Elliptic curve cryptosystems. We have presented the comparative study of multi-digit integer multiplication algorithms, and their performance is evaluated in terms of CPU execution time. Montgomery modular multiplication is a good practice, among others.

Big data and big risk: a four-factor framework for big data security and privacy

Big data refers to a very large volume of data with possibly varied and complex structure. With growing data processing and data analytic techniques, big data provides significant benefits to organisations and individuals by improving productivity and enriching people's life. However, security and privacy are big concerns for big data applications. While a large quantity of data is collected, securely storing, processing and using the data are challenging. In this paper, we propose a four-factor framework for big data security and privacy in business information systems. The proposed framework addresses big data security and privacy issues in terms of collecting the right data, collecting the right amount of data, protecting the data in the right way, and using the data for the right purposes. We present a set of approaches and models for each of the four factors to improve big data security and privacy.

Optimized Secure Federated Learning for Event Detection in Big Data using Blockchain Mechanism

Currently, cloud storage in blockchain and federated learning technology provides better security among data transmission and file access. But, in some of the cases, security issues arose. So, to avoid security problems and offer better protection in a cloud environment, a novel optimized buffalo-based Homomorphic SHA blockchain (OBHSB). In this model for accessing the cloud storage data with the key matching method, if any of the unauthenticated users are trying to access the file initially, the system checks the key matching parameter. The proposed model was developed to provide better security in big data presented in the cloud environment. However, the parameters in the proposed model were compared with the existing models to make sure better performance was attained through the proposed model. Attack was considered as an event in this research. In the performance analysis, the performance rate of the proposed model was validated. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design; model performs hashing, encryption, decryption, and key matching mechanisms. The results proposed model is observed to have 100% confidentiality rate after attack.

Studying the Security and Privacy Issues of Big Data in the Saudi Medical Sector

Studying the Security and Privacy Issues of Big Data in the Saudi Medical Sector

Retracted: Security and Privacy Risk Assessment of Energy Big Data in Cloud Environment.

[This retracts the article DOI: 10.1155/2021/2398460.].

Research on Personal Big Data Privacy Protection and Security Risk Prevention

Research on Personal Big Data Privacy Protection and Security Risk Prevention

Application of the cloud pyramid model and the SaaS method to increase the level of big data security

The digital revolution has changed the way, that business is conducted in many areas. Recently, large volumes of various types of data are used at high speed by a variety of sources, that are processed, often in real time, using information technology tools (powerful processors, software and algorithms. Today’s bank servers contain vast amounts of data collected from online interactions. People in modern times willingly share information about themselves. Traditional banking analytics systems aren’t capable of processing huge data sets from various sources. However, the development of digital technologies and big data analytics allow bank clients to structure and use their personal information to solve existing problems of saving bank data. It was determined, that every client of the bank is worried about the preservation of his personal information, which is provided to the bank managers. The essence of the concept of cloud computing, cloud infrastructure is defined, and the purpose of using cloud interfaces is also considered. Levels of problems, related to the security of saving bank customer data, have been revealed. The levels of opportunities for using the cloud pyramid to increase the level of security of big data in banking activities have been determined. The cloud enables the operation of an ideal platform with massive computing power and storage capacity to process big data with great variety, volume, veracity and speed. The SaaS provider takes care of the performance of applications, provides technical support for users, and installs updates independently. The type of services and the type of organizations, to which Software as a Service (SaaS) technologies can be applied, are considered. The role of Software-as-a-service (SaaS) technologies to increase the level of big data security in banking is defined.

Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

In recent years, it has been observed that the disclosure of information increases the risk of terrorism. Without restricting the accessibility of information, providing security is difficult. So, there is a demand for time to fill the gap between security and accessibility of information. In fact, security tools should be usable for improving the security as well as the accessibility of information. Though security and accessibility are not directly influenced, some of their factors are indirectly influenced by each other. Attributes play an important role in bridging the gap between security and accessibility. In this paper, we identify the key attributes of accessibility and security that impact directly and indirectly on each other, such as confidentiality, integrity, availability, and severity. The significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle process. To calculate the proposed work, researchers utilised the Fuzzy Analytic Hierarchy Process (Fuzzy AHP). The findings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare context. The study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

Privacy-Preserving Big Data Security for IoT With Federated Learning and Cryptography

Privacy-Preserving Big Data Security for IoT With Federated Learning and Cryptography

A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality

Data security and integrity are becoming increasingly important as the volume of data being created and stored grows. A controlled third party that provides most of the existing big data security systems makes them susceptible to several security risks. By resolving current technology challenges, including scalability, non-tampering, trustworthiness, data governance, and transparency, blockchain technology plays a vital role and has a significant potential to safeguard personal information. Therefore, this work focuses on addressing real-time big data storage issues based on a transdisciplinary research approach. This study introduces a brand-new approach to big data storage security that leverages blockchain technology and applies highway protocol to generate new blocks. The proposed highway protocol works based on the flexible finality condition to overcome issues of baseline models. The highway allows blocks to run the consensus mechanism to configure security thresholds more freely. The proposed protocol also allows blocks with lower thresholds to reach finality more quickly than blocks requiring greater degrees of confidence. Therefore, the proposed big data framework can dynamically control data manipulation and continuously support individuals to participate in the data-sharing process. A comparison was performed with the number of data requests in terms of hit ratio, and a highway protocol provides better results than baseline models. The proposed model provides a data processing period of 13 to 30 ms and an energy consumption of 32 to 41 mJ.

Research on Statistics Curriculum Reform under the Influence of Public Security Big Data Technology

In the use of big data, public security mainly focuses on the key processing of data. Although it is structured, it needs to be used in many aspects. Data processing results and analysis results, but through data processing, can effectively think about the specific implementation methods. Public security data science is becoming a new public security discipline and police technical field. Under the influence of public security big data technology, there are various problems in the development of statistics education and teaching and the design of teaching courses. On the premise of combining the design of statistics-related courses and the development of teaching, this paper makes an in-depth study on the reform of statistics courses under the influence of public security big data technology, and puts forward the reform path of statistics courses. In order to improve the teaching quality of statistics course and cultivate professionals who are more in line with the development of the times.

Framework architecture of a secure big data lake

This paper presents the architecture of a secure big data lake framework, the foundation of which is the Data Lake Architecture Framework (SDLAF). Security is entrusted to 2 components: Global Monitoring Tool (GMT), Check Sum Controller (CSC). The first one monitors data lake logs, automatically detecting anomalous data, the second one tracks the history of loading, processing, and unloading of file objects in the data lake. The conducted instrumental audit demonstrates both the effectiveness of the architecture and its prospects.

SECURING BIG DATA IN CLOUD WITH INTEGRATED AUDITING

In this paper, the review of the features of big data characteristics of cloud protection problem and research various security regulations and cloud domains were carried out. In order to perform security studies on availability and average time to failure of security, the stochastic process model was applied to analyze security. Based on this research, the use of integrated auditing for safe data storage and transaction logs is highly recommended, and transaction logs, enforcement and security reporting in real time, data climate, compliance with legislation, auditing of facilities, privacy, legality, management of identity and access, cyber threats, and granular auditing to achieve big data security advocated. The purpose of this research is to enforce the use of big data analytics for decision making and policy formulation in corporate and private organizations as well as create room for further research in cloud computing discipline. Keywords: Big data; security regulation; integrated auditing; cloud computing.

Chinese expert consensus on big data and real world study in cancer research (2022 edition)

Cancer is a public health and social problem to which great attention should be attached. Not only are there a large number of cancer patients in China, but great differences also exist in etiology, epidemiology, disease spectrum, and treatment between China and western countries. Therefore, tumor-related data in China has its own characteristics. Full reference to the data of western countries cannot accurately reflect the real situation of cancer prevention and treatment in China. If we can integrate, process and analyze Chinese data, and find rules in specific etiology, incidence, drug sensitivity and prognosis, it will play an important role in the formulation of health policy, medical research and disease prevention. The Society of Cancer Big Data and Real World Study of China Anti-Cancer Association organized multidisciplinary experts, combined with domestic and foreign literature and clinical practice, after repeated discussion and revision, finished this consensus including background, analysis and management, direction planning and operation flow, basic design, quality control standards, evidence level classification, data security and privacy standards of big data and real world study. The aim is to take full advantages of Chinese cancer big data to carry out high-quality real world study, and better promote the prevention and treatment of cancer in China.

The Security Challenges of Big Data Analytics: A Systematic Literature Review

The huge amount of data generated from heterogeneous sources such as social networking sites, healthcare applications, sensor networks and many other sources are drastically increasing from time to time swiftly. Big Data is described as extremely large datasets that have grown beyond the capability to manage and analyze them with traditional database processing tools. Big data analytics is the use of advanced analytical techniques against a very large heterogeneous datasets that include structured, semi-structured and unstructured data from different sources. The larger the quantity of data by itself is not advantageous unless analyzed to produce valuable information. This deluge amount of data creates an operational risk in which, the risks arise from storage devices, security of tools or the technologies used to analyze the data. In this paper, we perform a systematic literature review to give comprehensive review of security challenges and risks related to big data analytics. Security mechanisms such as cryptographic and non-cryptographic techniques are used to secure big data during analytics. The security of big data at rest and in transit gets enough investigation while a few researches had done at securing data at processing stage. Even though a number of possible techniques were proposed for big data security, it still suffers performance issues. This article is trying to explor security issues that used for preserving the Confidentiality, Integrity and Availability (CIA triad), non-repudiation as well as Access control in the context of big data analytics. Finally, we identify open future research directions for security of big data analytics. This paper also can serve as a good reference source for the development of modern security-preserving techniques to address various challenges of big data analytics security and privacy-issues.

Big Data Analytics in Patient Care During Coronavirus Disease 2019 (COVID-19) Pandemic: A Systematic Review

The latest advancements in the field of computational techniques and big data analytics (BDA) are very helpful in dealing with the spread of coronavirus disease 2019 (COVID-19). The objective of this study was to systematically review BDA applications that have been implemented in patient care and the challenges during COVID-19 pandemic. We conducted a literature search in PubMed, Science Direct, Clinical Key, EBSCO, ProQuest, Cochrane Central, Wiley’s Library, and Springer to identify relevant articles published from 2020 to 2022. Studies were included if they reported BDA applications that had been implemented in real-life patient care during COVID-19 pandemic. We identified 14 relevant studies that reported the implementation and evaluation of BDA applications in patient care during COVID-19 pandemic, of which ten adopted an observational design and four adopted an experimental design. The BDA applications targeted various tasks, such as estimate or predict risk score (n=4), diagnosis (n=6), and healthcare decision making (n=4). Regarding the challenges faced, we identified four issues of BDA during COVID-19, such as data governance, economic challenges, big data technology challenges, and security and privacy issues. This review indicates applications of BDA have tremendous potential to bring a lot of advantages, especially in combatting COVID-19 pandemic, despite insufficient evidence to advocate the routine use of patient care BDA.

Blockchain based Securing Medical Records in Big Data Analytics

The patient privacy is danger while medical records and data are transmitted or share beyond secure big data. This is because violations push them to the margins and they begin to avoid fully revealing their stages. This kind of stages contains negative impact in scientific investigate. To overcome this issue, Secure Block Chain System for Managing and Sharing Electronic Medical Records in Big Data Field is proposed. In this manuscript, a Cryptographic Hash Generator (CHG) technique based Secured and Trusted Data storage and transmission using Block Chain (BC) in Hadoop Distributed File System (HDFS). Initially, the Big data collected from the health care center is partitioned into sensitive and insensitive data. Block chain system utilizes an asymmetric cryptography for validating transactions authentication. Here, the user key is created through secured bitwise cryptographic hash generator (CHG) while there is required to fetch the newly record for usage. In block chain system, when a user seeking data from a healthcare application have forward a request to CHG. The message is send back to the user with a secret key for confirmation. The key can be decrypted or even denied access if only a valid user allows the user to link to this cluster. Only sensitive data were selected to the process of encryption for the process of encryption, this CHG technique employs the Discrete Shearlet Transform (DST) for encrypting the data, and the data’s are warehoused in the block chain to upgrade the level of security. And the insensitive data are put directly on the Hadoop Distributed File System. During the verification process, CHG is utilized for creating the request forward through the user. The operator creates the purpose of remote key to create the block (request) and signing the request using transaction private key, then forward to request queuing. To validate a request, the request from the queue is supplied first and an Improved Grey wolf Optimization algorithm (IGWO) is utilized to determine the optimal request that is fetch through the consensus node for initiating the process of validation. After accepting the user’s request, access is given to the user associated with input or requested data, then the verified request is set to broadcast. The proposed method is executed in JAVA and Hadoop platform. Experimental results show that the proposed BC-CHG-DST-IGWOA shows better performances of higher Efficiency 20.14%, 31.25%, 24.33%, 14.69%, lower time 16.12%, 15.09%, 21.36%, 46.26% compared with the existing methods, such as medical records managing and securing blockchain based system supported by genetic algorithm with discrete wavelet transform (BC-SMR-BD-GA-DWT), DQN-based optimization framework to secure shared blockchain systems (BC-SMR-BD-DQNSB), Hyper ledger blockchain enabled secure medical record management along deep learning-based diagnosis model (BC-SMR-BD-HBESDM-DLD), Secure attribute-based signature scheme with multiple authorities for blockchain in electronic health records system (BC-SMR-BD-MA-ABS) respectively.

Big data network security defense mode of deep learning algorithm

Abstract With the rapid development and progress of big data technology, people can already use big data to judge the transmission and distribution of network information and make better decisions in time, but it also faces major network threats such as Trojan horses and viruses. Traditional network security functions generally wait until the network power is turned on to a certain extent before starting, and it is difficult to ensure the security of big data networks. To protect the network security of big data and improve its ability to defend against attacks, this article introduces the deep learning algorithm into the research of big data network security defense mode. The test results show that the introduction of deep learning algorithms into the research of network security model can enhance the security defense capability of the network by 5.12%, proactively detect, and kill cyber attacks that can pose threats. At the same time, the security defense mode will evaluate the network security of big data and analyze potential network security risks in detail, which will prevent risks before they occur and effectively protect the network security in the context of big data.