Data Protection Scheme Research Articles

Deep Fuzzy Control-Based Network Blockchain Privacy Data Protection

Data protection is becoming more and more popular as a result of the ongoing growth of technologies like the Internet of Things and cloud computing. To better support this development, an increasing number of academics are delving further into the topic. The field of privacy protection technologies in the big data environment has steadily gained attention as data security measures have been expanded. Besides that, there are additional difficulties in safeguarding the privacy of various parties when it comes to data protection because data is frequently maintained by multiple parties. In this paper, we propose a network block chain privacy data protection scheme using depth fuzzy control. We achieve confidentiality of protected data by utilizing blockchain technology and deep fuzzy control algorithms. The deep fuzzy control method can be used in the aforementioned scheme to safeguard trading information while also enabling the interchange of protection data. Ultimately, the experiment is conducted in the context of cloud storage, and the findings validate the efficacy of the program.

The Privacy-Centric Data Pipeline : Strategies for Implementing Robust Security Measures in Data Engineering

This article presents a comprehensive framework for addressing data privacy and security challenges in modern data engineering environments. As organizations grapple with ever-increasing volumes of sensitive data and an evolving regulatory landscape, data engineers face complex challenges in safeguarding information throughout its lifecycle. We explore key components of a robust data protection strategy, including regulatory compliance, technical safeguards, and organizational practices. Our analysis covers critical areas such as encryption methodologies, access control mechanisms, data masking techniques, and lifecycle management policies. We also examine the role of data governance frameworks and the importance of continuous employee training in maintaining a secure data ecosystem. By synthesizing current best practices and emerging trends, this article provides data engineers and organizations with actionable insights for implementing effective privacy and security measures. Our findings underscore the need for a holistic approach that integrates technical solutions with strong governance practices to ensure data integrity, confidentiality, and compliance in an increasingly data-driven world.

Advancements and Applications of Anti-Fraud Graphical Passwords

Visual cryptography represents a sophisticated and innovative approach to secure information sharing, gaining traction within the field of information security. This technology adeptly transforms sensitive data into visually interpretable graphics or images. These images are then segmented into multiple independent parts, each distributed among different stakeholders. This strategy ensures that no single party can access the complete data without collaboration, thereby enhancing security. This dissertation delves into several anti-fraud visual password schemes designed to mitigate risks associated with deceptive behaviors by participants and distributors alike. By addressing these vulnerabilities, the proposed schemes enhance the integrity and reliability of the data sharing process. The application of visual passwords in real-life scenarios not only bolsters data security but also shields private information from potential leaks. By reducing the complexity and cost associated with traditional decryption methods, visual cryptography offers substantial support for a wide array of application scenarios. This dissertation presents a comprehensive exploration of visual cryptography's potential to revolutionize data protection strategies, illustrating its importance and utility in contemporary digital environments.

Optimizing Data Protection: Selecting the Right Storage Devices for Your Strategy

Choosing the appropriate storage devices is critical for optimizing data protection strategies. This article explores various storage options, including disk-based, deduplication, tape, network, cloud, and OST devices, offering insights into their ideal use cases. By understanding the benefits and applications of each storage type, administrators can make informed decisions to enhance their data protection and recovery efforts, achieving an effective 3-2-1 data protection strategy.

Federated learning data protection scheme based on personalized differential privacy in psychological evaluation

Federated learning enables multi-party model training by utilizing shared models instead of raw data, allowing for effective use of user data while ensuring privacy protection. However, the training process still has potential threats. Guided by the safe sharing and analysis of data in psychological evaluation, a federated learning data protection scheme based on differential privacy FL-PDP is proposed. First, local model parameters are perturbed using personalized differential privacy techniques to provide varying levels of privacy protection. Second, the privacy configuration selected by the user is divided into multiple levels and uploaded to the server after encryption to avoid targeted attacks in the uplink channel. Third, a probability-based selection method is employed for model aggregation to mitigate the impact of high privacy protection requirements on the model aggregation process. Finally, after aggregation is completed on the server side, noise that meets the global differential privacy threshold is added to quantify the level of global privacy protection. Experimental results indicate that this algorithm achieves high model accuracy, low communication latency, and scalability while ensuring privacy protection.

Data linkage in public health research: a scoping review of ethical, legal and societal aspects surrounding administrative data

ObjectiveThe application of data linkage for scientific research purposes in the public health sphere requires using personal and sensitive data on an individual level while results are analyzed from a population perspective. Considering this, ethical, legal and social issues (ELSI) must be considered. In this sense, our study aims to better understand how ELSI surrounding administrative data linkage use in public health has been addressed in the scientific literature. ApproachOur scoping review is based on the Joann Briggs Institute Manual for Evidence Synthesis and will be reported according to the Preferred Report Items for Systematic Reviews and Meta-Analyses – Scoping Review Guideline. Two independent authors searched for articles published between January 2013 and December 2023 in the PubMed/MEDLINE, Web of Science, EMBASE and LILACS databases, as well as in Scielo and Google Scholar platforms. Keywords used were related to ‘data linkage’, ‘administrative data’, ‘ELSI’, “public policies’ and ‘public health’ terms. Articles were screened initially by title and abstract, then by full-text read. ResultsAfter duplicate removal and articles screening, two independent authors are performing data extraction considering ethical, legal and regulatory basis as well as procedures for data access; data management process including record linkage; type of administrative data linked; data protection strategies; and data subjects rights. ConsiderationsThis scoping is underway, we intend to have our final results by April to present at the conference, in September.

Visually security privacy medical data protection scheme using compressive sensing and improved duffing chaotic system

Health and medical data frequently contain sensitive patient information that must be protected. Existing visual security schemes for medical images exhibit limitations in the imperceptibility of cipher images and the performance of image reconstruction. This paper introduces and evaluates a novel approach called Visual Meaningful Image Encryption (VMIE) for securing medical images. The proposed VMIE scheme employs a chaotic system based on the Duffing equation for initial encryption. Medical images are processed and encrypted in a sparse domain. A Bidirectional Chaotic Magic Transformation (BCMT) algorithm is then applied to scramble the sparse medical images. The scrambled data undergoes compression and diffusion. An adaptive embedding strategy employing the Discrete Cosine Stockwell Transform (DCST) integrates confidential data into the host image. The performance of the proposed chaotic system is validated through theoretical analysis and numerical simulation. Simulation results, along with comparisons to existing schemes, demonstrate the efficacy of the VMIE method in enhancing visual security and its suitability for natural images. The VMIE approach presented in this paper offers a promising solution for securing medical images, effectively addressing the limitations of current visual security schemes.

Guarding Privacy in the Digital Age: A Comparative Analysis of Data Protection Strategies in Indonesia and France

Personal data protection is a critical issue in the contemporary digital era, marked by a significant rise in data collection and processing by both governmental and private entities. Indonesia, with its recently enacted Personal Data Protection Law, faces substantial challenges in implementation and enforcement. In contrast, France, as an EU member state, adheres to the General Data Protection Regulation (GDPR), widely regarded as the global benchmark for data protection. This study employs a comparative analysis to examine the legal frameworks, enforcement mechanisms, and data subjects' rights in Indonesia and France. Data were gathered through an analysis of legal documents, policy reports, and case studies on regulatory implementation in both countries. The results reveal that while there are notable differences in regulatory approaches and specifics, both countries aim to safeguard individuals' data rights. France's GDPR provides a more comprehensive and structured framework, whereas Indonesia is in the nascent stages of developing and refining its data protection regulations.

Exploring the role of blockchain technology in enhancing data integrity and privacy protection

This paper systematically builds a theoretical framework for enhancing data integrity and privacy protection by analyzing the fundamentals of blockchain technology and its inherent characteristics, such as decentralization, tamperability, and cryptographic algorithms. The study empirically examines the transparency and anonymity balance mechanism of blockchain in handling sensitive data using case studies and simulation experiments, and at the same time, designs smart contracts to automatically implement data protection strategies for different types of data security threats.

Ensuring confidentiality and data security in economic analysis of business entities: Challenges and solutions

In today's world, where digitization permeates all spheres of our lives, the issue of confidentiality and data security becomes particularly relevant. The significant dependence of businesses on the processing and analysis of large volumes of data necessitates a complete understanding of the risks associated with their storage and processing. Given this circumstance, the present investigation focuses on identifying and scrutinizing the primary obstacles and measures to guarantee data confidentiality and security within the economic analysis of business entities. The topic's relevance is enhanced by the constant development of technologies and the increasingly palpable growth of cyber threats, which require the development of innovative approaches and methods of information protection. The analysis conducted in this study allows us to establish that the main challenges for ensuring data security include not only technical aspects but also the need to balance confidentiality and usability, as well as between centralized and decentralized data storage. It has been revealed that adequate confidentiality requires a comprehensive approach, including modern encryption technologies and authentication and the development of internal security policies and employee training programs. The study's conclusions underscore that ensuring data confidentiality and security requires coordinated interaction between technical solutions and organizational measures. Emphasis is placed on the importance of innovative technologies, such as artificial intelligence and blockchain, for enhancing security levels. The obtained results have significant practical implications for business entities, enabling them to develop and implement effective data protection strategies. For further research, it is proposed to focus on developing adaptive protection models capable of resisting the changing conditions of the digital environment.

Encryption techniques for financial data security in fintech applications

In the dynamic world of financial technology (Fintech), securing financial data is a key priority. Increasing digital connectivity, adoption of cloud-based services requiring complex measures to protect the integrity, privacy and availability of sensitive information. Encryption techniques are emerging as a key tool to achieve these goals about itself by converting plaintext into ciphertext, protected from unauthorized access and probability violations. This review paper examines the various encryption techniques required to secure financial information in fintech applications. The main methods described include symmetric encryption, asymmetric and hybrid encryption techniques. Additionally, the function of end-to-end encryption (E2EE) is discussed in terms of protecting data privacy while it is being sent, which is essential for safeguarding sensitive financial activities such as mobile banking and digital payments. With its sophisticated method of permitting calculations on encrypted data without the need for decryption, homomorphic encryption shows promise for facilitating safe data analysis in Fintech settings while preserving data confidentiality. Each encryption method is scrutinized in terms of its strengths, weaknesses and practical applications in Fintech. Considerations such as computing efficiency, scalability, and regulatory compliance are addressed to provide insights for optimizing data protection strategies while adhering to industry standards and regulatory frameworks. The future of Fintech security is expected to be shaped by new developments in encryption technology, including post-quantum cryptography, artificial intelligence integration for adaptive security measures, and privacy-preserving solutions. The goal of these advancements is to strengthen the robustness of financial data security techniques in an increasingly linked digital world while mitigating changing cyber risks.

DATA PROTECTION IN THE AGE OF SOCIAL REGULATION: A CONCEPTUAL FRAMEWORK

In the current digital landscape, characterized by rapid technological advancements and pervasive social media usage, the intersection of data protection and social regulation has emerged as a critical area of focus. This research paper explores how emerging social norms and regulatory frameworks shape the ways in which personal data is collected, processed, and protected. It critically examines the evolving definitions of privacy and consent, the role of regulatory bodies in enforcing data protection standards, and the implications of social expectations on corporate practices. By presenting a conceptual model that integrates legal, ethical, and societal dimensions, the article highlights the necessity for a multifaceted approach to data protection that transcends traditional legal frameworks. It argues for a collaborative paradigm that engages stakeholders, including individuals, institutions, and policymakers, in the creation of robust data governance solutions. Ultimately, this work aims to contribute to the discourse on building resilient data protection strategies that are aligned with contemporary social values and technological realities. This article aims to analyze the effectiveness of the Personal Data Protection Act of 2023 in ensuring the right to privacy and data protection.

From Barriers to Boosters: Optimizing Open Government Data through Publishing Guidelines and Data Protection Strategies

Open Government Data (OGD) is a global endeavor, a collaborative effort between governments worldwide to share datasets that encapsulate a wide spectrum of government activities, from environmental issues like pollution and climate to social aspects like education and childcare, and urban concerns like traffic and congestion, and healthcare statistics. As governments, being among the largest producers and collectors of data, are making OGD available online in diverse formats, primarily Word, PDF, or Excel, they are contributing significantly to this global initiative. The OGD initiative holds immense potential to revolutionize the way we access and use government data. Its primary objective is to enhance the discoverability, accessibility, and availability of data in alternative and preferably machine-readable formats. This, in turn, empowers a diverse set of stakeholders to develop innovative data applications under licensing schemes that permit unrestricted reuse. Despite these promising aspects, challenges such as data heterogeneity, data protection, data quality, and data provenance issues persist. This study aims to analyze and categorize these challenges and obstacles that hinder the OGD initiative from realizing its full potential, with a particular emphasis on data protection and security concerns for data providers.

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.

Blockchain Interoperability in Data Exchange Logistics Integration

Logistics companies are increasingly adopting private blockchains for enhanced data management because of the trends in cooperation. Nevertheless, this practice poses new challenges concerning the security and sharing of data. The real-time nature and diversity of logistics data increase the difficulty of protecting the data. Additionally, when transportation information changes, downstream enterprises must promptly adjust their production plans to accommodate these alterations. The strict access controls of private blockchains can obstruct downstream enterprises from obtaining data, posing a challenge to the overall operational efficiency. In this paper, we propose an innovative logistics data protection scheme that employs private set intersection (PSI) and blockchain cross-chain technology to achieve data security. In our scheme, logistics companies within the logistics consortium are added as trusted agents to the public blockchain, enabling downstream enterprises to acquire logistics data integration from the public blockchain. Utilizing an RSA-based PSI protocol, our approach enhances exchange efficiency while protecting private data without transmitting additional information. We evaluate the performance of the proposed solution through a series of experiments, and the results demonstrate that our solution can achieve secure and efficient logistics data exchange.

Design and Implementation of an Automated Disaster-Recovery System for a Kubernetes Cluster Using LSTM

With the increasing importance of data in modern business environments, effective data management and protection strategies are gaining increasing research attention. Data protection in a cloud environment is crucial for safeguarding information assets and maintaining sustainable services. This study introduces a system structure that integrates Kubernetes management platforms with backup and restoration tools. This system is designed to immediately detect disasters and automatically recover applications from another Kubernetes cluster. The experimental results show that this system executes the restoration process within 15 s without human intervention, enabling rapid recovery. This, in turn, significantly reduces the potential for delays and errors compared to manual recovery processes, thereby enhancing data management and recovery efficiency in cloud environments. Moreover, our research model predicts the CPU utilization of the cluster using Long Short-Term Memory (LSTM). The necessity of scheduling through this predict is made clearer through comparison with experiments without scheduling, demonstrating its ability to prevent performance degradation. This research highlights the efficiency and necessity of automatic recovery systems in cloud environments, setting a new direction for future research.

International trade privacy data management system combining Internet of Things blockchain

Currently, international trade (Int’l Trade) often faces risks such as privacy data leakage and transaction security during transactions, which seriously affect the protection of privacy data in trade. Therefore, this article used the Internet of Things and blockchain to attempt to address these risk issues and improve the security of privacy data. By analyzing the security threats in Int’l Trade transactions, this article used additive aggregation functions to analyze the protection effect of privacy data and studied the core issues of Int’l Trade privacy data. This article analyzed the privacy protection model using the Internet of Things blockchain and proposed corresponding Int’l Trade privacy data protection strategies. Among the five surveyed enterprises, data audit and data backup were all above the standard value, while data query only exceeded the standard value for enterprises 2, 3, 4, and 5. The average abnormal value of the enterprise in data anomalies decreased by 0.06 compared to the standard value. Through experimental testing, it has been found that Internet of Things blockchain can effectively improve the privacy data protection effect of Int’l Trade and promote the convenience of Int’l Trade transactions.

A novel medical image data protection scheme for smart healthcare system

AbstractThe Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks.

Research on privacy and secure storage protection of personalized medical data based on hybrid encryption

AbstractPersonalized medical data privacy and secure storage protection face serious challenges, especially in terms of data security and storage efficiency. Traditional encryption and storage solutions cannot meet the needs of modern medical data protection, which has led to an urgent need for new data protection strategies. Research personalized medical data privacy and secure storage protection based on hybrid encryption, in order to improve the security and efficiency of data storage. A hybrid encryption mechanism was proposed, which uses user attributes as keys for data encryption. The results show that the storage consumption of user attribute keys increases with the number of user attributes, but the consumption of hybrid encryption privacy storage technology is much smaller than that of traditional schemes. In the test, when the number of users increased to 30, the processing time first reached 1200 ms. During the increase in data volume, both test data and real data showed a brief decrease in attack frequency, but after the data volume reached 730–780, the attack frequency increased. It is worth noting that the performance of test data is better than that of real data. Personalized medical data privacy and secure storage protection based on hybrid encryption can not only effectively improve data security and reduce the risk of attack, but also greatly outperform traditional solutions in storage consumption and processing time. It has important practical significance for modern medical data storage protection.

Data Security Encryption Analysis Based on Blockchain Trusted Big Data Artificial Intelligence

Abstract The rapid development of Internet of Things (IoT) technology brings convenience to people but also brings data security problems. The data security problem can be solved effectively by blockchain due to its non-tampering and decentralization features. The purpose of traceability and auditable verification is achieved by adding user identity information and access policy to the blockchain in this paper. A blockchain-based data security access mechanism for cross-organizations is proposed and an attribute-based access control model is constructed. The feasibility and efficiency of this data protection scheme are evaluated by deploying an experimental environment and comparing it to reference schemes. The scheme proposed in this paper consumes less Gas for contract deployment than in other studies, and the Gas consumption grows slowly with more nodes, i.e., the cost grows slowly. The scheme in this paper consumes 7.00 seconds and 3.05 seconds less time than the reference network approach as well as the BAC approach in executing 300 transactions, respectively. The proposed scheme in this paper has shorter attribute secret key generation time and encryption/decryption time than the reference scheme. For example, when the number of attributes is 50, the scheme in this paper reduces the time of secret key generation by 144ms, 64ms, and 348ms compared to the scheme 1, 2, and 3. This paper breaks the performance bottleneck of blockchain and provides a new scheme for the security of user’s data and encryption.