Data Security Problems Research Articles

How to Promote Data Sharing of Financial Service Providers Based on Federated Learning Technology

Data sharing between financial service providers (FSPs) can promote the reduction of financial risks, but the reality is that the FSPs may be unwilling to share data with each other due to data security problems. The emergence of federated data technology provides a convenient way to address this issue. In this article, the author proposes using a federated data sharing model for FSPs and two dynamic evolutionary games for evaluating influencing factors. Cooperation results when both FSPs reach a certain level of mutual payoff. The reward and punishment mechanisms are important means by which FSPs actively implement real data sharing. The interaction between the FSP and the federated technology service provider (FTSP) demonstrates the practicality of this new technology. The results show that the data sharing improved the data trust and supported regulatory mechanisms.

Securing UAV Communications with Homomorphic Encryption and Key Regeneration: A Model Checking Approach

Unmanned Aerial Vehicles (UAVs) are at the centre of multiple applications across various fields, including delivery, disaster response, and surveillance. However, its use of wireless communications exposes them to security risks such as information distortion and interception. The paper presents a security model based on Homomorphic Encryption (HE) and Key Regeneration (KR) with the incorporation of Model Checking to enhance UAV communication security. Our system employs homomorphic encryption that resolves the data security problem by ensuring data security at the transmission stage, where information is processed at the receiver end without decryption, thus maintaining privacy and the data processing policy at the UAV end. Furthermore, the key regeneration protocol updates the encryption keys regularly, thus reducing the risk denial window and impact in case of key compromise. The security of the protocols is proven through model checking that establishes the system status operation through a systematic study, as per the provided security requirements. The simulation results indicate certain trade-offs in the system with performance degraded in encryption time and computations, unlike the existing system. The system enhancement outweighs the performance, as indicated by the minimized level of threat and integrity violation. Therefore, integrating HE and KR can secure UAV communication in the presence of a smart and capable adversary, making the entire system robust and dependable in the line of operation.

Research and Design of Data Encryption Circuit based on D Flip-Flop

Since information technology is developing so quickly, the problem of data security has become increasingly prominent. Data encryption technology, as an important means of securing data transmission, plays a vital role in resisting network attacks and protecting privacy. In this paper, a data encryption circuit based on D flip-flop is studied, aiming to improve the efficiency and security of data encryption. First, this paper introduces the basic principles of data encryption and analyzes the application of D flip-flop in encryption circuit. Subsequently, the design principle of Moore’s state machine is elaborated, and encryption circuit is designed with the use of the state graph and state table. Further, this paper demonstrates the logic circuit design process., including the derivation of D-flip-flop input Circuit construction and Boolean expressions. This research provides a hardware implementation design solution for the design of data encryption circuits, which is expected to play a role in improving encryption speed and enhancing security.

Peningkatan Literasi Teknologi IoT untuk Mendukung Implementasi Smart City di Masyarakat Perkotaan

The implementation of Internet of Things (IoT) technology in the smart city concept has become an important innovation that plays a major role in improving the quality of life of urban communities. IoT enables the integration of various interconnected devices and systems to optimize the management of city resources such as energy, transportation and security. This research aims to encourage the influence of IoT technology on the implementation of smart cities in several cities in Indonesia. Using qualitative and quantitative methods, this research collects data through interviews with city stakeholders as well as secondary data analysis from related reports and publications. The research results show that the application of IoT in smart cities has a significant impact on increasing operational efficiency, better environmental management, and improving public services. However, this research also found a number of challenges, such as limited infrastructure, data security problems, and low digital literacy among the public. In conclusion, although IoT has great potential in accelerating the implementation of smart cities, policies are needed that support and increase society's capacity to reach the full potential of this technology.

Data Security Issues in Intelligent Cloud Computing Systems

This project takes the intelligent cloud computing system as the main research object. It mainly uses the literature analysis method to explore the data security problems in the intelligent cloud computing system. By investigating the development background of intelligent cloud computing, the author summarizes the definition of cloud computing and the characteristics of cloud computing. The author also makes a specific analysis of the advantages of cloud computing, and concludes that cloud computing is possible through safe and reliable data, low demand for clients, shared cloud data and unlimited possibilities. These four points are the main reasons major enterprises choose cloud computing to replace traditional computing resources. In the topic, the author studied the data security problems in cloud computing from three aspects: the problems exposed in the development process of cloud computing, the effective measures to protect data security in cloud computing and predicting the future development trend of improving data security. The authors believe that future research should focus on a balance between network security issues in cloud computing and building a secure and reliable data system.

Forms of Expression of Will in Civil Transactions

Introduction: the relevance of the topic is related to the emergence of new electronic forms of expression of will when making transactions, which has necessitated ensuring their security in the digital environment. The purpose of the study is to solve the problems of the form of expression of will of parties to civil transactions, including transactions concluded in electronic form by means of information and communication systems. Methods: in preparing the study, the dialectical, logical, and comparative law methods were used, which made it possible to analyze various approaches to classifying forms of expression of will, including oral, written, and other forms, and their legal significance in the context of current legislation. Results: the paper analyzes various forms of expression of will that are important for the validity and enforceability of transactions. The doctrine and judicial practice related to the digitalization of the expression of will have been studied. The problem of ensuring the protection of the rights of parties to electronic transactions related to the identification and authentication of the parties in the digital environment is analyzed. The ways to improve the regulatory framework to take into account the development of digital technologies and ensure the protection of the rights of parties to transactions are proposed. The scope of the results: the approaches outlined in the study to solving the problems of expression of will in the digital environment can become the basis for improving the current civil law legislation; they can serve as the basis for further scientific research in this area; and they can improve law enforcement practice. Conclusions: the proposals related to solving the problems of identification and authentication when entering into the transaction are substantiated. It is proposed to use blockchain technology to solve the problem of confidential data security on the one hand and transparency on the other. The need to fix in the law the methods of electronic transmission of legally significant messages is justified.

Toward a Secure and Private Cross-Chain Protocol Based on Encrypted Communication

Blockchain technology is becoming more prominent and is being used in many different industries. Data islands have emerged as a result of the difficulty in transferring assets and exchanging information between blockchains because of differences in the underlying technology. Cross-chain technology is becoming increasingly prevalent as a solution to the data security problem. Decentralized blockchain networks frequently use the Hashed Timelock Contract (HTLC) to solve the problem of balancing atomicity and time sensitivity. However, it suffers from drawbacks such as limited security and privacy protection capabilities. To overcome these limitations, a secure and fully functional system named the Exchange Smart Contract (ExchangeSC) has been developed; the ExchangeSC can integrate smart contracts and Paillier homomorphic encryption into the Mid-Account HTLC (MA-HTLC) cross-chain protocol. This integration effectively resolves the problem of low security and privacy protection in the HTLC cross-chain protocol. Specifically, the locked information in the solution is encrypted using homomorphic encryption before uploading to the blockchain, which is operated by participating nodes in the ciphertext domain. The ExchangeSC demonstrates reasonable performance on the official testing network’s EVM platform. Further evaluation of the ExchangeSC-based HTLC cross-chain reveals its superior security and lower time cost compared to the BitXHub cross-chain project.

A GSO‐based multi‐objective technique for performance optimization of blockchain‐based industrial Internet of things

SummaryThe latest developments in the industrial Internet of things (IIoT) have opened up a collection of possibilities for many industries. To solve the massive IIoT data security and efficiency problems, a potential approach is considered to satisfy the main needs of IIoT, such as high throughput, high security, and high efficiency, which is named blockchain. The blockchain mechanism is considered a significant approach to boosting data protection and performance. In the quest to amplify the capabilities of blockchain‐based IIoT, a pivotal role is accorded to the Glowworm Swarm Optimization (GSO) algorithm. Inspired by the collaborative brilliance of glowworms in nature, the GSO algorithm offers a unique approach to harmonizing these conflicting aims. This paper proposes a new approach to improve the performance optimization of blockchain‐based IIoT using the GSO algorithm due to the blockchain's contradictory objectives. The proposed blockchain‐based IIoT system using the GSO algorithm addresses scalability challenges typically associated with blockchain technology by efficiently managing interactions among nodes and dynamically adapting to network demands. The GSO algorithm optimizes the allocation of resources and decision‐making, reducing inefficiencies and bottlenecks. The method demonstrates considerable performance improvements through extensive simulations compared to traditional algorithms, offering a more scalable and efficient solution for industrial applications in the context of the IIoT. The extensive simulation and computational study have shown that the proposed method using GSO considerably improves the objective function and blockchain‐based IIoT systems' performance compared to traditional algorithms. It provides more efficient and secure systems for industries and corporations.

Uncovering the Issues Associated with AI and other Disruptive Technology Enabled Operational Practices in Healthcare Sectors in India

Background: Advanced technologies, including artificial intelligence (AI) and other Disruptive technology, have been directly responsible for the significant changes, renovations, and enhancements that have taken place in healthcare systems around the world. In spite of the many challenges, particularly in nations still growing their economies, the healthcare industry has a significant number of opportunities. Objective: To explore the key obstacles that were encountered by the healthcare industry both during and after the introduction of AI and other Disruptive technological practices associated with Health 4.0 in the healthcare industry to uncover how these variables influence AI and other Disruptive technology adoption in healthcare sector of India. Methods: An online survey format that included standardized questionnaire data was obtained from 83 hospitals, and a total of 434 samples have been implemented for various healthcare administrative staff members by the adoption of AI and Disruptive technology. ANOVA analysis was done to confirm the hypotheses' assumptions, then descriptive statistics were done to analyze the mean value, and also EFA and CFA analysis with SEM analysis has been done in the SPSS program for numerous validity tests (version 20). Results: This research explored 15 issues that healthcare administration staff members consider barriers and through the use of EFA, only two of the three primary obstacles"Additional workload" and "Problems with adopting technology" have a substantial impact on the rate at which AI is adopted in the healthcare industry, as seen through the eyes of the workers in that area. Conclusion: These challenges include a high need for capital, extra investments in new technologies like the internet of things (IoT), artificial intelligence (AI), business analytics, , resistance to change from both employees and employers, the need for a skilled workforce, and the upkeep of support systems. The use of blockchain technology in India's healthcare system as a secure service for administrative workers in Health 4.0 Practices could solve data security problems.

Performance Evaluation of Laguerre Transform and Neural Network-based Cryptographic Techniques for Network Security

As the world evolves day by day with new technologies, there is a need to design a secure network in such a way that intruders and unauthorized persons should not have access to the network as well as information regarding the personnel in any firm. In this study, a new cryptographic technique for securing data transmission based on the LaplaceLaguerre polynomial (LLP) is developed and compared to an existing auto-associative neural network technique (AANNT).The performance of the LLPT and AANNT was tested with some selected files in a MATLAB environment and the results obtained provided comparative information (in respect of AANNT versus LLPT) as follows: encryption time (1.67 ms versus 3.9931s), decryption time (1.833 ms versus 2.1172s), throughput (26.2975 Kb/s versus 0.01098 Kb/s), memory consumption (3.349 KB versus 15.958 KB). From the compared results, it shows that AANNT offers a faster processing time, higher throughput, and takes up less memory space than the LLPT. However, cryptanalysis of the AANNT is possible if the network's weight and design are known; hence, the technique is unreliable for ensuring the data integrity and confidentiality of encrypted data. The proposed LLP cryptographic algorithm is designed to provide a higher security level by making the LLP algorithm computationally tedious to invert using the standard Laplace transform inversion method. When compared to the AANN-based cryptographic technique, cracking the algorithm to uncover the encryption key takes time. This shows the strength and robustness of the proposed LLP cryptographic algorithm against attacks, as well as its suitability for solving the problem of data privacy and security when compared to the AANN-based cryptographic algorithm.

Research on Information Data Security of University Archives

Under the background of the rapid development of information technology, the information technology of university archives has become an important part of the modernization of education management. However, in this process, the problem of data security has become increasingly prominent, which has become a major obstacle restricting the in-depth development of university archives informatization. In the face of security threats such as data leakage and network attacks, this paper proposes a series of systematic protection countermeasures, aiming at providing a safe and reliable data environment for university archive informatization, ensuring the integrity and availability of information resources, and thus promoting the improvement of university management efficiency and the optimal allocation of education resources.

A Novel Framework for Cloud Data Security with Blockchain Technology and Distributed Virtual Machine Agents

Even though cloud computing has advanced over the years, the two major challenges currently faced by cloud computing are data security and trusted computing. In cloud computing applications, blockchain technology can enhance data security and trusted computing when paired with virtual machine agents and mobile agent technologies. A decentralized new framework for distributed computing is blockchain. A potential area of study is the integration of blockchain technology with cloud computing, leveraging the former's security mechanisms to enhance the latter's secure computing and storage capabilities. The usage of the proposed framework builds on the inherent security features of blockchain technology in an effort to secure data and information integrity, confidentiality and authentication in cloud environments. Through the introduction of distributed virtual machine agents, the framework increases the scalability and effectiveness of data protection mechanisms for virtual data environments that can be used to store information protected by cloud security measures. Thus, the proposed study substantiates the applicability and efficiency of the existing and emerging problems of cloud data security and this approach develops satisfactory data protection solutions.

Energy-aware dynamic rescheduling of flexible manufacturing system using edge-cloud collaborative decision-making method

ABSTRACT With the development of cloud computing technology, energy-aware scheduling based on cloud computing is an essential means to achieve energy saving and carbon reduction in manufacturing systems. Due to various production disturbances in flexible production, the scheduling based on cloud computing has data security problems and poor real-time performance. How to use real-time information to improve the security and responsiveness of cloud scheduling is a research gap. This paper establishes a cloud-edge collaborative dynamic flexible job-shop energy-aware rescheduling decision-making model. According to the characteristics of dynamic production in a flexible manufacturing system, create a learning model for cloud scheduling with Deep Q Network (DQN) and send the training model to the edge for scheduling decisions. Based on real-time production interference data, edge scheduling model real-time update scheduling scheme. To improve the robustness of the cloud scheduling model based on DQN, Dynamic scheduling data at the edge will be uploaded to update the cloud model. In addition, the effectiveness and practicability of the model are verified in an extrusion workshop. The experimental results show that this method can improve the comprehensive objective evaluation of minimum energy consumption and completion time by 3.6% −49.3% compared with the traditional scheduling rules.

Localization and Mapping for Self-Driving Vehicles: A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicle localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains.

Advanced Access Controls and Deduplication for Privacy in Multimedia Cloud Computing

This work provides a privacy-preserving multi-dimensional media sharing system, SMACD, for portable cloud computing scenarios within the context of widespread media sharing enabled by cloud computing and devices. Attribute-based encryption is used to jumble each media layer, ensuring media privacy and granular access control. To reduce the complexity of the get-to arrangement and adapt to the properties of multi-dimensional media, a multi-level get-to arrangement development with a secret-sharing plot is presented. For both intra-server and inter-server deduplication, decentralized key servers are offered. With cloud computing, databases and application software are moved to sizable data centers, where data and service management may not be entirely reliable. However, this special feature brings up a number of new, poorly understood security challenges. Due to the fact that both user data and applications are on provider premises, cloud computing raises data security problems as neither is entirely contained on the user's machine. Although clouds often have a single architecture, they can have numerous customers with various needs. The solution provided by all cloud providers is to use encryption methods to encrypt the data. To solve every issue, there's also a potential that the cloud service is unreliable

Secure Genomic String Search with Parallel Homomorphic Encryption

Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications.

ROLE OF DIGITAL TECHNOLOGIES IN TRANSFORMATION OF ECONOMICS, FINANCE AND PUBLIC ADMINISTRATION

This article examines new technologies that enable government and business to accelerate their digital transformation and even change its scope. Digitalization is inextricably linked to the innovative development model of both private company and state in order not to lag behind in the global technology race. With the rapidly developing digital economy and the increasing use of artificial intelligence, the problem of applying these technologies in public administration is becoming more and more urgent. The development of effective strategies and methods for integrating new technologies into public administration processes is crucial for increasing the efficiency and transparency of government and various economic processes, improving the provision of services to citizens, and ultimately for ensuring sustainable and balanced development of society and the economy. Nevertheless, the application of such technologies in public and private administration brings with it a number of challenges and problems. For example, it is related to ethical and legal issues of using artificial intelligence, the problem of data security and cybersecurity, as well as the need to train qualified specialists capable of working with these technologies. In this context, the relevance of the topic of this article is determined by the need for an in-depth study of the potential for the application of digital transformation technologies in public administration, economics and finance.

Analyzing of Modified DES-64 Algorithm for Securing the Data on File Storage Systems

Rapidly developing technology makes it easier for us to get information from processed data. The authenticity of a data is a very basic thing of information. With the rapid development of technology, there are more and more threats such as manipulate, phishing or even deleting data that will be processed into information without authentication. Guaranteeing the confidentiality and authenticity/integrity of data in the form of files is very important to produce accurate information and can be accounted for by way of encryption or one way function for authentication. The goal is of course to make it difficult for other parties to change it. The ultimate goal of this study is to implement the modified des 64 algorithms, to compare the avalanche effects and randomness test values from the standard DES test results with modified DES on the S-Box and P-Box sections which are made dynamic depending on the key, so that the data stored is encrypted. The modification process is by entering several key combinations including the DES weak key, alphabet and alphanumeric. The implementation is carried out on document file storage with a user authentication process and file encryption with a key that uses sha-256 to generate a different key for each file. When the owner downloads the data, the decryption process is executed, so that the security and authenticity of the data can be guaranteed. With the modified 64-bit DES, it is expected that data security problems on storage can be decreased or even solved.

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.

Forecasting Order Delays with Artificial Intelligence-Based Applications

The digitalization trend in the world in the last two decades has begun to transform business processes. Departing from the traditional business approach, the use of technology in every field has changed business processes and, accordingly, the approach of managers. On the other hand, cloud computing, the internet of things, sensors and instant notification systems through these components brought by the last industrial revolution have transformed supply chains into a smart structure. Now, the information that a product sold by a retailer, which is the last link of a supply chain, is out of stock is transmitted to the raw material supplier in the first link of the chain. In this way, a perfect supply chain structure is formed and operations are carried out in lean working principles. The products prepared according to the reduction of the retailer's stocks are continuously supplied, which shows flawless operation. On the other hand, through digital components such as sensors and the Internet of Things, each step in workflows is instantly converted into data and stored in virtual environments called cloud computing. At this point, besides the problem of data storage and security, the main problem is to process the data in a meaningful way. Analyzing big data generated by AI-based components and systems is also possible with AI-based systems. Methods called machine learning have been developed for this situation and their application area is increasing day by day. In the light of this information, within the scope of the study, forecasts of order quantity delays for future periods using historical order data from customers for the products produced by an enterprise were analyzed using machine learning algorithms. The results of the analysis made through the Microsoft Azure Machine Learning Studio platform will contribute to the use of digital tools in the sector as well as increasing the application examples of machine learning. According to the results obtained as a result of the analyzes, recommendations were developed for the enterprise. Finally, suggestions for the application of machine learning in the field of business administration are presented.