Data Security Problems Research Articles

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.

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.

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.

Health data security sharing method based on hybrid blockchain

With the rapid development of Internet of Things(IoT) technology, it is more and more common to use IoT devices to obtain health data. Health data sharing is a crucial component of health data utilization, yet it encounters serious privacy and security challenges. How to solve the problem of data security is the key factor to encourage users to participate, and existing data sharing methods generally have the problem of single point of failure. Based on this, this paper proposes a hybrid blockchain based health data sharing method HSHB, which divides sharing transactions into private chain data sharing and alliance chain data sharing according to different sharing entities. According to the identity and access purpose of different entities, HSHB has constructed the health data access control policy HDAC to avoid sharing interference among different entities. By building a b+ tree index of health data, HSHB provides users with an efficient and stable query scheme for historical health data. In addition, agent re-encryption and smart contract technology are used to share health data among institutions while ensuring the security of health data. Finally, the HSHB scheme is tested on XuperChain. The experimental results show that the health data sharing scheme HSHB proposed in this paper has advantages in computing overhead, throughput, and latency.

The Influence of Steganographic Methods and QR Code Resolutions on Data Hiding in Cover Images

Abstract This work deals with digital image processing using the steganography technique in real life. Various steganography techniques face data authentication, data security, and copyright issues. Due to the fact that the problem of data security is currently very topical, these techniques are often sought after. This is related to the constant development of new algorithms that provide solutions that will safely hide the secret message in the image and at the same time will be resistant to various types of attacks. The motivation for writing this work lies in the technique of representing a secret message using a Quick Response code. Later in this work is comparison between our results and results of approach made by scientific community, which works with data hiding within steganographic principles. The secret message is written in the least significant bit plane of the cover image, which ensures that the modification of the image cannot be detected by the naked eye. The proposed solution is evaluated by mathematical quality assessment parameters Peak signal-to-noise ratio, Mean squared error, Normalized cross-correlation and Structural similarity index.

Secure Storage of Computer Network Data based on Cloud Computing

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources. User’s data is stored in large database. The stored data can be accessed and modified by the clients over the Internet. The data is monitored by the Third Party Auditor (TPA) on behalf of the client. Therefore, integrity is lacked by the data stored on the servers. The data integrity is ensured by the cloud services that provide trust to the privacy of users. Aiming at the urgent problem of network data security in cloud computing operations, this paper proposed a security situation assessment system to grasp the security situation in real time. A set of cloud computing network data storage security is proposed. Through this model, the extraction of network data storage security situation elements, the design of network data storage security situation assessment scheme and the calculation method of network data storage security situation value are completed. The experimental results show the error of the predicted value obtained by the network data storage security situation assessment system. The effectiveness of the system model and the superiority of the improved algorithm is verified by the experimental results. This paper uses the cloud model to predict the cloud computing network security situation. On the other hand, the security situation value obtained by the situation assessment process can be used directly without training the original situation value. Performance improvement by the proposed technique over existing technique is seen and it is observe that the proposed technique is 23% and 34% better than the existing techniques.

Data Security Issues and Countermeasure Suggestions for Financial Big data: A Literature Review

Financial Big Data is a contentious topic that needs to be addressed because it is having an increasingly negative impact on people's everyday lives, work habits, and thought patterns. The leakage of financial big data is just one of the issues that financial big data is now dealing with. As a result, the study focus of this work is on the data security problems and solutions related to financial big data. This paper summarizes four main categories of data security issues related to financial big data: attacks on financial big data, data leaks related to financial big data, issues with the reliability of financial big data, and issues with access control related to financial big data. Based on these four problem categories, it searches the literature and organizes the suggested solutions as follows: pay attention to the development of financial data security systems in a big data environment, strengthen education and training, increase the da-ta security awareness of various roles, optimize the processing of big data, and strengthen the storage security of big data by strict user access control. This paper's thought and research also reveal that while financial big data does provide data security issues, it also serves as the primary means of addressing the issue itself. Less research has been done on using financial big data as a countermeasure in and of itself. In the future, we should focus on financial big data itself and explore ways to use financial big data as a countermeasure to data security problems.

Use of digital transformation and artificial intelligence strategies for pharmaceutical industry in Pakistan: Applications and challenges

The widespread application of artificial intelligence (AI) technology in Industry 4.0 can improve the work efficiency of doctors and help more patients. The application direction of AI in pharmaceutical logistics can be roughly divided into two types: First, AI technology empowers intelligent devices, and second, it improves labor efficiency through software systems. This article summarizes the digital development of the pharmaceutical industry in the United States, the European Union, and Japan, analyzes the current status and shortcomings of digital development in Pakistan’s pharmaceutical industry, and proposes digital development strategies for Pakistan’s pharmaceutical industry. The development of the domestic Internet has promoted the reform and progress of the pharmaceutical industry, but the policy development of digital applications needs to catch up. The digital transformation of the pharmaceutical industry faces problems such as the need for digital technology support for new drug research and development, medical data security problems, and the need for service platform construction for pharmaceutical enterprises. Finally, this article provides feasible suggestions for the better application of AI technology in Pakistan’s pharmaceutical industry by reviewing the research status, development trends, and development bottlenecks of AI technology in this area.