Big Data Security Research Articles

MatSwarm: trusted swarm transfer learning driven materials computation for secure big data sharing

The rapid advancement of Industry 4.0 necessitates close collaboration among material research institutions to accelerate the development of novel materials. However, multi-institutional cooperation faces significant challenges in protecting sensitive data, leading to data silos. Additionally, the heterogeneous and non-independent and identically distributed (non-i.i.d.) nature of material data hinders model accuracy and generalization in collaborative computing. In this paper, we introduce the MatSwarm framework, built on swarm learning, which integrates federated learning with blockchain technology. MatSwarm features two key innovations: a swarm transfer learning method with a regularization term to enhance the alignment of local model parameters, and the use of Trusted Execution Environments (TEE) with Intel SGX for heightened security. These advancements significantly enhance accuracy, generalization, and ensure data confidentiality throughout the model training and aggregation processes. Implemented within the National Material Data Management and Services (NMDMS) platform, MatSwarm has successfully aggregated over 14 million material data entries from more than thirty research institutions across China. The framework has demonstrated superior accuracy and generalization compared to models trained independently by individual institutions.

Extreme learning with projection relational algebraic secured data transmission for big cloud data

SummaryCloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA‐SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA‐SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets‐001 dataset in Java. ELPRA‐SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

Strengthening big data security and privacy in libraries: A Review of ISO standards and real-world cyberattack case studies

This paper presents a review of ISO standards and real-world case studies to provide a robust framework for enhancing big data security and privacy in libraries. The study synthesises relevant ISO standards such as ISO/IEC 27001, ISO/IEC 27014, and ISO/IEC TS 19608 and analyses library cyberattacks to identify vulnerabilities and security gaps. The paper provides practical recommendations and mitigation strategies particularly beneficial for librarians in resource-limited regions and developing countries. By integrating these standards with case study insights and addressing security requirements across the big data lifecycle, the paper offers an actionable guide to help libraries strengthen their cybersecurity frameworks, improve risk management, and maintain public trust.

Privacy Protection Guarantee For Data Hacking Victims According To Indonesian Law

Personal data leaks have become one of the major issues in cybercrime. The government has attempted to overcome this issue by implementing Law on Personal Data Protection (UU PDP) and establishing law enforcement agencies for Personal Data Protection. Despite the existence of laws that regulate and agencies responsible for this issue, personal data leaks still occur frequently. One of the cases was the leak of customer data from Bank Syariah Indonesia (BSI). This study analyzes the assurance of privacy rights concerning data managed by Big Data Controllers under the Personal Data Protection Law and identifies legal actions that victims of data hacking due to the data controller’s negligence regarding the Big Data security systems. This research uses normative juridical methods with a descriptive-analytical approach. The results of the research show that Indonesia has regulations to protect the privacy rights of its citizens, specifically in the Personal Data Protection Law which regulates actions and restoration of the right to privacy if data leaks occur by the limitations of this article.

Mixed fragmentation technique for securing structured data using multi-cloud environment (MFT-SSD)

Large data storage security is a topic of great interest to researchers, particularly in the age of big data where preserving data from theft, unauthorized access, and storage failure has become a crucial concern. To safeguard such data, encryption/decryption approaches have been employed, which are time-consuming and inefficient. The aim of this study is to develop a method, namely Mixed Fragmentation Technique for Securing Structured Data using Multi-Cloud Environment (MFT-SSD), for protecting large-scale data stored in a multi-cloud environment. This prevents insider attacks by adopting a mixed fragmentation approach to split the data into three files. For example, healthcare data is will be distributed among many clouds, each of which stores a partially unrecognized fraction of data without the need for an encryption or decryption layer. Comparing MFT-SSD to various encryption/decryption algorithms, our results show significant improvement; hence, the total performance of big data security is also improved.

Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security

In the age of information, the proverb "knowledge is power" has been shown to be true. admission to, which ultimately leads to knowledge acquisition. The relevance of the ability to glean knowledge from vast amounts of facts has increased. To describe the process of distributing, storing, and gathering enormous amounts of data for future analysis, researchers coined the term "big data analytics" (BDA). Data is generated at an alarming rate. The Internet of Things (IoT), the net's explosive expansion, and other technological advancements are the main forces behind this long-term growth. Since the information generated reflects the environment in which it is formed, the use of information gleaned from systems to understand the inner workings of those systems. The goal of protecting assets has been developed into a crucial component of cybersecurity. Additionally, big data now has the status of a high-value target due to the growing value of data. Current cybersecurity research in relation to big data has been reported here to explore big data security and its potential use as a cybersecurity tool. This gives trends, open research projects, and challenges along with a summary of current studies in the form of tables. In addition to current advancements and unanswered questions in this area of active research, this research work also provides readers a more thorough understanding of safety in the big data era.

Secure Big Data Model Based on Blockchain Technology

Secure Big Data Model Based on Blockchain Technology

A prototype-assisted clustered federated learning for big data security and privacy preservation

In the rapidly expanding field of IoT, data production has reached an unprecedented scale, providing valuable insights that accelerate decision-making processes. However, ensuring the privacy and security of this massive amount of data poses significant challenges. In this paper, we propose using clustered federated learning (CFL) as a solution to ensure both the security and privacy of big data by uploading model weights while keeping the data stored locally. Nevertheless, there are practical challenges in applying CFL to big data: (1) the participating FL clients are unlikely to have identical data distributions; (2) insufficient attention is given to the similarity between different clusters; and (3) CFL tends to ignore the class imbalance problem (i.e., long-tailed), which hinders its application in big data and affects the quality of target tasks. To address these issues and enable widespread CFL deployment in big data applications, this paper proposes a prototype-assisted clustered federated learning framework (MDSPFL). It relaxes the assumption of unique data distribution for each client, allowing the client’s local dataset to follow multiple source distributions considering classification class imbalance, thereby aligning with clients in a big data environment. Specifically, MDSPFL employs the proximal update mechanism to handle workload surges caused by mixed distribution and unavailability of similarity between cluster models. Additionally, MDSPFL introduces a class-balanced local training mechanism to resolve the long-tailed problem, which utilizes contrastive learning and class prototypes to enforce a uniform distribution of all classes in the feature space. We conduct extensive experiments on different datasets (EMNIST, Cifar10, Cifar100), and the experimental results demonstrate the effectiveness of our proposed MDSPFL in big data scenarios with imbalance and mixed-distribution clients.

RiskTree: Decision Trees for Asset and Process Risk Assessment Quantification in Big Data Platforms

The inherent characteristics of big data lies in its voluminous scale, varied data formats, and swift processing velocity. The intrinsic characteristics of big data undermine the efficacy of conventional data security techniques and data management standards, consequently compromising the security of big data. As a consequence, big data possesses susceptibilities to security incidents, including unauthorized data access, data manipulation, and data compromise throughout the transmission, storage, and processing stages. Conventional information system security risk assessment methodologies are constrained by human resources and computational techniques, rendering them unsuitable for direct application to big data platforms. Consequently, there is an urgent necessity to develop a risk assessment framework tailored specifically for big data environments, capable of quantifying potential risks and losses. In response to this need, we have devised an automated risk assessment theory that assimilates the unique characteristics of big data with traditional quantitative methods, introducing a risk metric system suited to the big data context. Utilizing the risk-related data generated during operations on the big data platform, we train a decision tree model to derive the weights for each risk indicator. These weights are then employed to conduct a weighted summation of the operational risk indicators, thereby achieving a quantitative evaluation of the platform's risk profile. To substantiate the proposed framework, experiments were conducted on a simulated big data platform. The experimental outcomes demonstrate that, compared to existing quantitative risk assessment methodologies, our approach enables an automatic, objective, and efficient assessment and quantification of the risks associated with tangible assets and data processing operations within the big data platform.

Security and Information Technology for Big Data

Big data has grown in importance in a variety of corporate and academic fields as a result of substantial changes in human behavior and lifestyles. While this focus simplifies data management, big data is regarded as important in many industrial areas because it can be merged with other types of data. Big data can be managed alongside other types of data, particularly in large organizations that handle a range of data types. This type of management contributes to the improvement and development of the organizational structure, as well as raising the skill level of employees in these organizations. Despite these benefits, there are numerous concerns, particularly throughout the stages of data gathering and storage, when there is a risk of hacking or leakage, as well as the difficulty of sorting and identifying data. For these reasons, big data protection is regarded as one of the most important issues discussed in this paper in order to develop an appropriate framework for protecting big data related to companies, as well as methods for dealing with this data in order to develop an appropriate strategy to protect big data security and privacy.

An intelligent optimized secure blockchain mechanism for cloud auditing

The cloud auditing model was attractive for the cloud storage system to check the integrity of the stored user files. Hence, to maximize the security concern, federated learning and the blockchain were introduced. But, in some cases, security issues arose because of the extensive unstructured data and attack vulnerability. So, to avoid security problems and offer better protection in a cloud environment, a novel Optimized Buffalo-based Homomorphic SHA Blockchain (OBHSB) is proposed. In this model for accessing the cloud storage data with the critical 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. Hence, federated learning was adopted to create the cloud auditing phase in the distributed cloud environment. Therefore, it gives flexibility in training all data types: audio, text, and images. Moreover, the prime performance parameters were calculated and compared with other traditional approaches to justify the efficiency of the proposed model. The attack was considered an event in this research. The performance rate of the proposed model was validated in the performance analysis. Subsequently, the case study was developed in this research to explain the working procedure of the proposed design, and the result of the proposed model was discussed.

Big Data Privacy Protection and Security Provisions of the Healthcare SecPri-BGMPOP Method in a Cloud Environment

One of the industries with the fastest rate of growth is healthcare, and this industry’s enormous amount of data requires extensive cloud storage. The cloud may offer some protection, but there is no assurance that data owners can rely on it for refuge and privacy amenities. Therefore, it is essential to offer security and privacy protection. However, maintaining privacy and security in an untrusted green cloud environment is difficult, so the data owner should have complete data control. A new work, SecPri-BGMPOP (Security and Privacy of BoostGraph Convolutional Network-Pinpointing-Optimization Performance), is suggested that can offer a solution that involves several different steps in order to handle the numerous problems relating to security and protecting privacy. The Boost Graph Convolutional Network Clustering (BGCNC) algorithm, which reduces computational complexity in terms of time and memory measurements, was first applied to the input dataset to begin the clustering process. Second, it was enlarged by employing a piece of the magnifying bit string to generate a safe key; pinpointing-based encryption avoids amplifying leakage even if a rival or attacker decrypts the key or asymmetric encryption. Finally, to determine the accuracy of the method, an optimal key was created using a meta-heuristic algorithmic framework called Hybrid Fragment Horde Bland Lobo Optimisation (HFHBLO). Our proposed method is currently kept in a cloud environment, allowing analytics users to utilise it without risking their privacy or security.

Guest Editorial XAI Based Biomedical Big Data Privacy and Security

Guest Editorial XAI Based Biomedical Big Data Privacy and Security

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

An Intelligent Big Data Security Framework Based on AEFS-KENN Algorithms for the Detection of Cyber-Attacks from Smart Grid Systems

Privacy Challenges and Solutions in Big Data Analytics: A Comprehensive Review

Abstract: Large data collections with a more intricate and diversified structure are referred to as "big data." These traits are typically associated with more challenges when it comes to storing, analyzing, and applying additional procedures or getting results. The technique of analyzing vast quantities of intricate data to uncover patterns or connections that are concealed is known as "big data analytics." Nonetheless, there is a clear conflict between big data's extensive use and its security and privacy. This paper examines privacy needs in big data, distinguishes between privacy and security, and focuses on privacy and security issues in large data. This study addresses the applications of privacy in business by utilizing established techniques like HybrEx, k-anonymity, T-closeness, and L-diversity. Many privacy-preserving techniques have been created for protection of privacy at various phases of the big data life cycle (such as data generation, storage, and processing). This work aims to explain the issues facing current privacy preservation strategies in big data and to give a comprehensive evaluation of them. This study also introduces new approaches to privacy preservation in big data, such as quick anonymization of massive data streams, identity-based anonymization, differential privacy, privacy preserving big data publishing, and hiding a needle in a haystack. This study discusses big data privacy and security issues in healthcare. A comparative analysis of some contemporary large data privacy approaches is also conducted. A comparative analysis of the latest big data privacy approaches is also conducted.

INNOVATIVE PEDAGOGICAL APPROACHES FOR TEACHING BIG DATA SECURITY IN HIGHER EDUCATION: A REVIEW

This study critically examined the existing literature to identify key themes, challenges, and best practices and provided comprehensive analysis of the evolving strategies and practices employed in higher education to teach big data security. in teaching big data security in higher education. The review emphasized the importance of integrating practical experiences, interdisciplinary approaches, and ethical considerations into the curriculum, ensuring stakeholders are well prepared to address the complexities of securing large scale data systems. By highlighting the significance of the industry collaborations and the need for standardized frameworks for teaching big data security, this study offers valuable insights for educators, curriculum developers, and stakeholders in higher education.

A Survey on Network Security Traffic Analysis and Anomaly Detection Techniques

With the increasingly severe network security situation, advanced network traffic anomaly detection techniques are urgently needed. This paper provides a comprehensive survey of the research status and latest progress in the field of network anomaly detection. Firstly, we introduce the basic concepts, common methods, and challenges of network traffic analysis, which lays the foundation for anomaly detection. Then, we systematically summarize the mainstream techniques in the anomaly detection field, including statistical methods, machine learning methods, deep learning methods, and behavior analysis methods, analyzing their basic principles, representative works, advantages and disadvantages, and applicable scenarios. Next, we focus on discussing the hybrid methods in the anomaly detection field, elaborating on the motivations, common strategies, and representative works of hybrid methods, and pointing out that hybrid methods are an important development direction for anomaly detection. In addition, the paper also summarizes the application effects of several types of methods in practical network security tasks and makes a quantitative comparison in tabular form. Finally, we prospect the future development trends of network anomaly detection techniques, proposing goals such as intelligentization, automation, federalization, and interpretability, while analyzing the challenges faced by anomaly detection, including data heterogeneity, complexity of security threats, model robustness, privacy protection, and interpretability. We argue that network anomaly detection requires interdisciplinary integration, strengthening of security big data governance, and a shift from passive defense to active immunity. As the strategic position of cyberspace security becomes increasingly prominent, driven by disruptive technologies such as big data, artificial intelligence, and blockchain, network anomaly detection will surely usher in new development opportunities and challenges.

Enhancing Big Data Security through Comprehensive Data Protection Measures: A Focus on Securing Data at Rest and In-Transit

Purpose: This research paper aims to enhance Big Data security by implementing comprehensive data protection measures, focusing on securing data at rest and in transit. In the era of Big Data, organizations handle vast quantities of data characterized by high velocity, volume, and variety, which complicates management and increases security risks. Methodology: The study examines various data protection strategies, including encryption, access control, data masking, immutable storage, tokenization, and physical security for data at rest. For data in transit, it explores encryption protocols, secure transfer methods like SSH and TLS, VPNs, Zero Trust architecture, and secure APIs. These methods are crucial for safeguarding sensitive information and preventing unauthorized access. Findings: The findings highlight common security challenges in Big Data, such as data breaches, unauthorized access, and integrity issues. The study emphasizes the need for robust protection measures and offers a comprehensive view of the data security landscape. Implementing these strategies helps organizations safeguard sensitive information and ensure compliance with international data protection regulations, enhancing their overall security posture. Unique contribution to theory policy and practice: This paper contributes to theory, policy, and practice by advocating comprehensive data protection strategies. It stresses the importance of continuous monitoring and regulatory compliance, providing practical insights into best practices and technologies that protect Big Data. The research supports developing robust data protection policies and practices, advancing knowledge in Big Data security.

Developing an integrated framework for risk management in policing: crisis management using big data analytics as a case study

Objective: The study seeks to develop an integrated framework for enhancing risk management and crisis management in policing through the utilization of big data analytics, using the Dubai police force as a case study. Theoretical Framework: This research is grounded in the intersection of big data analytics and risk management within the field of policing, focusing on how data-driven approaches can innovate and improve security crisis management. Method: Data for the study was collected via questionnaires distributed to 450 police officers across all departments in Dubai. The analysis was conducted using AMOS software within a Structural Equation Modeling framework to assess the impact of big data analytics on police risk and crisis management. Results and Discussion: The results indicate that big data analytics significantly enhances risk management and crisis management in policing. It was found that police risk management serves as a mediating factor between big data analytics and security crisis management. These findings suggest that the application of big data can substantially improve knowledge and operational performance in police organizations. Research Implications: The study emphasizes the crucial role of big data analytics in revolutionizing police risk and crisis management processes. It highlights the potential for these technologies to provide substantial improvements in the efficiency and effectiveness of police operations. Originality/Value: This research is valuable as it provides empirical evidence of the benefits of big data analytics in the context of policing. It offers practical guidelines to police departments, particularly within the UAE, on leveraging big data to enhance their operational performance in managing risks and crises. The study contributes to the broader understanding of integrating advanced data analytics into public safety and emergency response strategies.

Big data security and privacy in healthcare: A systematic review and future research directions

Big data security involves protecting healthcare data from unauthorised use and access and making any changes, while privacy entails ensuring the confidentiality of individual patient information within the datasets. The two are crucial in ensuring that big data is effectively applied for research and personalised care, compromising the sensitivity of healthcare data and medical information. Consequently, as the amount of data in the healthcare sector grows exponentially, securing sensitive data becomes increasingly crucial while effectively utilising big data analytics. The systematic review explores the issues and challenges associated with big data security and privacy in healthcare. Through reference to resource-based view theory, this paper seeks to examine the present state of research in this area, identify gaps in the existing literature, and propose strategies for future research. The search strategy looked at the data between 2013 and 2022 and yielded 93 studies from four databases: Scopus, PubMed, ScienceDirect and IEEE Xplore. From these studies, 18 papers are reviewed. The findings reveal that while significant progress has been made in securing healthcare data, numerous challenges still need to be considered. Overall, this review highlights the importance of continued research to ascertain that sensitive healthcare data is kept secure while allowing for the enhanced application of big data analytics. It contributes to the security and privacy problems associated with using big data in healthcare, measures for addressing these challenges, the healthcare value of big data, and foci for future research.