Information Privacy Research Articles

Impact of the Quality of Financial and Banking Applications Used on Smartphone on the Customer Satisfaction of Jordanian Islamic Bank

This study aimed to identify the impact of the quality of financial and banking applications through the dimensions of (information security, trust, privacy) used on smartphones on the satisfaction of Jordanian Islamic bank customers. The researcher employed a descriptive-analytical approach in constructing the study. The study population consisted of all customers of Jordanian Islamic banks. The researcher developed a research tool (a questionnaire) to align with the study’s dimensions. A random sample of Islamic bank customers was selected, and the research tool was distributed to them electronically. The researcher was able to obtain responses from 249 participants. One of the key findings of the study was that there is a high level of usage of financial and banking applications on smartphones in Islamic banks. Among the dimensions, “privacy” ranked the highest in importance based on the participants’ responses. The results also revealed a significant impact of the quality of financial and banking applications used on smartphones, through the dimensions of (information security, trust, and privacy), at a significance level of (α ≤ 0.05) on the satisfaction of Jordanian Islamic bank customers. Among the key recommendations was the need for greater focus on quality factors related to financial and banking applications by emphasizing the dimensions of information security, trust, privacy, and data protection due to their significant role and impact on customers using these applications. Additionally, there is a potential for further research and studies on the factors affecting the quality of financial and banking applications on smartphones for electronic banking services offered to customers.

Navigating The Duality: Privacy And Security Concerns In Knowledge Management Systems (KMS)

This study delves into the intricate relationship between privacy and security within Knowledge Management Systems (KMS). It identifies and analyzes distinct employee perceptions of these intertwined issues within a company's KMS environment. By exploring these perspectives, the authors aim to dispel the confusion surrounding privacy and security in KMS settings. The research employed an explanatory sequential mixed method design. First, a questionnaire survey was distributed directly to KM staff across three companies. This was followed by semi-structured interviews with four KM staff members. The findings reveal a high level of employee awareness regarding the importance of KMS and, consequently, the significance of personal information privacy and security. The study further distinguishes between privacy and security concerns within KMS. Privacy concerns, differentiated across three dimensions: confidentiality, trust, and behavior, are primarily viewed from the organizational layer. Security aspects, on the other hand, are seen as aligned with the ICT layer, governed by legal frameworks and KMS architecture.

Networking and need of complex literacy in the digital age

The digitalisation of society is a process that is constantly improving. The main reason for this is the development of contemporary technologies (Cloud Computing, Mobile Cloud, Internet of Tings, cyber-Physical Systems, Big Data, etc.) which impose new requirements on working conditions and the necessary knowledge for their use. This requires improving the digital literacy of society by expanding the knowledge and skills to use technologies and to know the need to comply with rules for information security, privacy and personal data protection. In this regard, the article discusses the problem of the need for complex literacy by adding new components to the established understanding of information (digital) literacy, covering the additional requirements for working in the global space. Based on a retrospective analysis of publications in the field of information literacy, the need for a new understanding of digital literacy is substantiated and a structure of the so-called "Complex Literacy" is proposed and the requirements for the new components "Technological Literacy" and "Security & Privacy Literacy” whit extended discussing the issues of information security, privacy and personal data protection in the digital age.

SecureCloud: A Shield Against Virtual Threats

Cloud computing is a constituent of today and tomorrow innovative information and computing technologies which provides useful and convenient services like storage and access etc. But it has important problems which aggravate with security aspects of a system with regards to the data confidentiality and integrity. These challenges are met in this paper by conducting a detailed theoretical study of literature on data security risks in single and multi-cloud models. Some of the threats include; break-ins, theft of information and loss of information. A comparison of extant literature on security measures like encryption and access control in the cloud based systems is done. It is shown that the solutions available currently have certain deficits, which contribute to the necessity of enhancing safety features. Thus, to fill these gaps, it is suggested to have one layer of protection against data access and another one in relation to data downloads. That way it is protective of the data, useful and clients’ information privacy will be well protected. Thus, the results of the study can be valuable for researchers and cloud service providers who are working on the improvement of the information security in cloud environments

PHR-NFT: Decentralized Blockchain Framework with Hyperledger and NFTs for Secure and Transparent Patient Health Records

Blockchain technology holds significant promise for healthcare by enhancing the security and integrity of patient health records (PHRs) through decentralized storage and transparent access. However, it has substantial limitations, including problems with scalability, high transaction costs, privacy concerns, and intricate stakeholder access management. This study presents PHR-NFT, a novel framework that strengthens PHR privacy by utilizing Hyperledger Fabric and non-fungible tokens (NFTs) to address these issues. PHR-NFT improves privacy and communication by letting patients keep control of their medical records while permitting temporary, permission-based access by medical professionals. PHR-NFT offers a transparent solution that increases trust among healthcare stakeholders through the robust and decentralized architecture of the Hyperledger Fabric. This study demonstrates the viability and effectiveness of the PHR-NFT framework through performance evaluations focused on transaction latency, throughput, and security. This research has valuable implications for enhancing data privacy and security in healthcare practices and insightful information about blockchain-based healthcare systems.

Mitigating smart contract vulnerabilities in electronic toll collection using blockchain security

The Internet of Vehicles (IOV) is a distributed network that provides several services based on vehicle information (e.g., location, speed), such as Electronic Toll Collection (ETC). ETC has been introduced to replace traditional toll booths, where vehicles need to line up to pay, especially during peak travel times. The main advantage of ETC is improved traffic efficiency. However, existing ETC systems often fail to secure the privacy of vehicle information and are vulnerable to fund theft. This makes automatic payments inefficient and susceptible to attacks like the Reentrancy attack.In this paper, we leverage the Ethereum blockchain and smart contracts to facilitate automatic payments within the ETC system. The primary challenges addressed include authenticating vehicle data, automatically deducting fees from users’ wallets, and safeguarding against Reentrancy attacks in smart contracts, all while maintaining the confidentiality of distance-related information necessary for fee calculation. To address these concerns, we implement a decentralized application featuring a comprehensive end-to-end verification algorithm that operates at both entry and exit toll points, incorporating robust measures to protect sensitive distance data from potential leaks.Results show that the accuracy of fees remains relatively high, with reasonable execution times. Additionally, our system’s gas consumption is more efficient compared to related works, making transactions more cost-effective. These outcomes demonstrate that the proposed system not only secures transactions but also ensures correct and efficient payment services, positioning it as a viable solution for improving the security and functionality of ETC systems.

Evaluating the Ethical Frameworks of Information Security Professionals: A Comparative Analysis

Information Technology (IT) networks have become a cornerstone of business communication, evolving alongside the Internet, which now facilitates the interconnection of independent IT infrastructures. This interconnectedness has increased the risks of information disclosure and privacy violations, presenting ethical challenges for information security professionals. This study aims to evaluate the ethical codes of four major information security professional organizations in computing sciences to identify critical ethical considerations for these professionals and highlight areas for potential improvement. The study analyzes the ethical frameworks and guidelines provided by these organizations, examining their alignment with the multidimensional aspects of security, including technical, organizational, legal, social, and ecological factors. The analysis reveals that information security extends beyond technical measures, encompassing broader organizational and societal dimensions. Ethical concerns cannot be effectively addressed without engaging all stakeholders. While existing codes of ethics and security policies are valuable, they require updates to address the evolving challenges of the information society. For effective information security, professional codes of ethics must integrate comprehensive stakeholder considerations and adapt to meet the growing demands of a connected digital world. Enhancing these ethical frameworks is crucial for fostering trust and resilience in the information society.

XSShield: A novel dataset and lightweight hybrid deep learning model for XSS attack detection

With the proliferation of web applications, cross-site scripting (XSS) attacks have increased significantly and now pose a significant threat to users' information security and privacy. To enhance the efficiency of XSS attack detection, the adoption of machine learning (ML) and deep learning (DL) techniques offers promising solutions, but their effectiveness is limited by the lack of comprehensive and diverse datasets. Moreover, existing approaches often prioritize detection accuracy over real-time processing capabilities, which are essential for effective defense. To address these challenges, in this paper, we propose a novel framework that automatically collects web resources, efficiently extracts informative features, and constructs an up-to-date XSS attack dataset, which is then used to train a machine learning-based XSS detection model. Using this framework, we created and published a well-structured dataset over 100,000 samples for the research community. Furthermore, we present a hybrid detection model that leverages the strengths of both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. Extensive evaluations of our dataset demonstrate that the proposed model outperforms other baseline ML models across various metrics, including processing rate. Notably, our model achieves an accuracy of 99.27% while maintaining a low false positive rate of 0.06% and high processing rate of exceeding 1000 samples per second. These results highlight its high accuracy and robustness in detecting XSS, and suitability for real-time applications. Our work presents a comprehensive solution for enhancing web application security by providing a diverse dataset and a high-accuracy detection model with low latency.

Large-scale scattering-augmented optical encryption

Data proliferation in the digital age necessitates robust encryption techniques to protect information privacy. Optical encryption leverages the multiple degrees of freedom inherent in light waves to encode information with parallel processing and enhanced security features. However, implementations of large-scale, high-security optical encryption have largely remained theoretical or limited to digital simulations due to hardware constraints, signal-to-noise ratio challenges, and precision fabrication of encoding elements. Here, we present an optical encryption platform utilizing scattering multiplexing ptychography, simultaneously enhancing security and throughput. Unlike optical encoders which rely on computer-generated randomness, our approach leverages the inherent complexity of light scattering as a natural unclonable function. This enables multi-dimensional encoding with superior randomness. Furthermore, the ptychographic configuration expands encryption throughput beyond hardware limitations through spatial multiplexing of different scatterer regions. We propose a hybrid decryption algorithm integrating model- and data-driven strategies, ensuring robust decryption against various sources of measurement noise and communication interference. We achieved optical encryption at a scale of ten-megapixel pixels with 1.23 µm resolution. Communication experiments validate the resilience of our decryption algorithm, yielding high-fidelity results even under extreme transmission conditions characterized by a 20% bit error rate. Our encryption platform offers a holistic solution for large-scale, high-security, and cost-effective cryptography.

PCDP-CRLPPM: a classified regional location privacy-protection model based on personalized clustering with differential privacy in data management

Abstract Location data management plays a crucial role in facilitating data collection and supporting location-based services. However, the escalating volume of transportation big data has given rise to increased concerns regarding privacy and security issues in data management, potentially posing threats to the lives and property of users. At present, there are two possible attacks in data management, namely Reverse-clustering Inference Attack and Mobile-spatiotemporal Feature Inference Attack. Additionally, the dynamic allocation of privacy budgets emerges as an NP-hard problem. To protect data privacy and maintain utility in data management, a novel protection model for location privacy information in data management, Classified Regional Location Privacy-Protection Model based on Personalized Clustering with Differential Privacy (PCDP-CRLPPM), is proposed. Firstly, a twice-clustering algorithm combined with gridding is proposed, which divides continuous locations into different clusters based on the different privacy protection needs of different users. Subsequently, these clusters are categorized into different spatiotemporal feature regions. Then, a Sensitive-priority algorithm is proposed to allocate privacy budgets adaptively for each region. Finally, a Regional-fuzzy algorithm is presented to introduce Laplacian noise into the centroids of the regions, thereby safeguarding users’ location privacy. The experimental results demonstrate that, compared to other models, PCDP-CRLPPM exhibits superior resistance against two specific attack models and achieves high levels of data utility while preserving privacy effectively.

Digital Transformation and Innovation of the News Media

Under the background of globalization and the rapid development of information technology, the digital transformation and innovation of news media have become an important trend of the industry. Traditional media adapt to the new audience needs and market environment through digital transformation, and gradually transform to diversified content production, personalized recommendation, multi-platform distribution and paid subscription mode. Many media organizations, such as the New York Times and the BBC, have explored the possibilities of content innovation and dissemination through artificial intelligence, big data and virtual reality. However, digital transformation also faces challenges such as information authenticity, privacy security and technology dependence. In the future, with the further development of new technologies, news media will continue to explore AI-driven news production, immersive reporting and blockchain applications to build a more innovative, efficient and credible news ecosystem.

SecureCloud Guardian: Advancing Data Security via Advanced Dispersion and Encryption in Cloud Strorage (SCG)

Cloud storage, being one of the foundational pillars of modern computing, has been readily embraced and spurred fast growth in the cloud computing domain. Despite this massive and widespread acceptance, major security issues hang over the cloud storage scene and often result in huge security breaches and leaks of information. SecureCloud Guardian, or SCG, comes into the picture as an important instrument that guarantees privacy in information stored over the cloud. SCG follows a multi-pronged approach, including data dispersal techniques, storage distribution protocols, and encryption formats that guarantee efficient data storage. It also employs a hierarchical management approach that leverages user-level secret code as well as secret division algorithms for maintaining cryptographic material, preventing unauthorized access. The experimental studies carried out on SCG verify the effectiveness of this security-centered cloud storage technology in terms of avoiding data leakage. For instance, SCG uploads and downloads 5G files in a mere 646 seconds and 269 seconds, respectively—an attractive choice for consumers looking for secure cloud information storage.

Validity and reliability of the Turkish version of the Information Concealment Scale for Caregivers of palliative care patients.

This methodological study aimed to establish the validity and reliability of the Turkish version of the Information Concealment Scale for Caregivers of palliative care patients. The study was conducted between January and June 2023 with 155 caregivers who cared for patients hospitalized in the palliative care units of 2 hospitals in Istanbul, Turkey. Exploratory factor analysis and confirmatory factor analysis were performed for validity analysis. Cronbach's α, item-total correlation, intraclass correlation coefficient (ICC), and Pearson correlation analysis were used for reliability analysis. Of the participants, 54.2% were female and 69% were married. The mean age was 37.96 ± 12.25 years. According to the exploratory factor analysis, the scale consisted of 3 subscales and 15 items. The first subscale of the scale was expressed as "misrepresentation of the disease'; the second subscale was defined as "concealment of information"; the third subscale was defined as "misrepresentation of the real situation." As a result of the modifications made in confirmatory factor analysis, the goodness-of-fit values were as follows: CMIN/DF(X2/Sd) = 175.16/815 = 2.16; GFI = 0.88; CFI = 0.91; RMSEA = 0.079; RMR = .070; NFI = 0.90. The Cronbach's α values of the subscale were between 0.79 and 0.87. ICC values were between 0.90 and 0.95 at a confidence interval of 95%. A positive correlation was determined between the subscales. It was determined that the Turkish version of the Information Concealment Scale was a valid and reliable tool for caregivers.

Patient Health Record Protection Beyond the Health Insurance Portability and Accountability Act: Mixed Methods Study.

The security and privacy of health care information are crucial for maintaining the societal value of health care as a public good. However, governance over electronic health care data has proven inefficient, despite robust enforcement efforts. Both federal (HIPAA [Health Insurance Portability and Accountability Act]) and state regulations, along with the ombudsman rule, have not effectively reduced the frequency or impact of data breaches in the US health care system. While legal frameworks have bolstered data security, recent years have seen a concerning increase in breach incidents. This paper investigates common breach types and proposes best practices derived from the data as potential solutions. The primary aim of this study is to analyze health care and hospital breach data, comparing it against HIPAA compliance levels across states (spatial analysis) and the impact of the Omnibus Rule over time (temporal analysis). The goal is to establish guidelines for best practices in handling sensitive information within hospitals and clinical environments. The study used data from the Department of Health and Human Services on reported breaches, assessing the severity and impact of each breach type. We then analyzed secondary data to examine whether HIPAA's storage and retention rule amendments have influenced security and privacy incidents across all 50 states. Finally, we conducted a qualitative analysis of textual data from vulnerability and breach reports to identify actionable best practices for health care settings. Our findings indicate that hacking or IT incidents have the most significant impact on the number of individuals affected, highlighting this as a primary breach category. The overall difference-in-differences trend reveals no significant reduction in breach rates (P=.50), despite state-level regulations exceeding HIPAA requirements and the introduction of the ombudsman rule. This persistence in breach trends implies that even strengthened protections and additional guidelines have not effectively curbed the rising number of affected individuals. Through qualitative analysis, we identified 15 unique values and associated best practices from industry standards. Combining quantitative and qualitative insights, we propose the "SecureSphere framework" to enhance data security in health care institutions. This framework presents key security values structured in concentric circles: core values at the center and peripheral values around them. The core values include employee management, policy, procedures, and IT management. Peripheral values encompass the remaining security attributes that support these core elements. This structured approach provides a comprehensive security strategy for protecting patient health information and is designed to help health care organizations develop sustainable practices for data security.

Digital twin-based cross-enterprise production-delivery synchronization in a highly dynamic environment

Efficient collaboration between production and logistics is crucial for timely order fulfillment and stable operations of system, necessitating closer cooperation between manufacturers and third-party logistics providers. However, achieving cross-enterprise synchronization presents three main challenges. First, concerns regarding information privacy and security lead to disjointed and non-shared resources. Second, the decision-making is not coordinated and consistent in dealing with dynamic interference. Third, differentiated strategies emerge as manufacturers and 3PLs employ different batch processing methods for customer orders in pursuit of economies of scale. This multifaceted disconnect leads to significant asynchronization in cross-enterprise production-logistics, resulting in inefficiencies and increased costs throughout the system. To address these challenges, this paper focuses on three key aspects: a digital twin-based cross-enterprise information-sharing framework, a cross-enterprise synchronized mechanism, and a collaborative optimization based distributed decision method. These aspects provide an intelligent production-logistics synchronization solution in the unique environment of Industry 4.0. Finally, through a case study of a paint enterprise, this paper comprehensively evaluates the differences in performance of the Production-Logistics Synchronization System (PLSS) under various synchronization modes. The results indicate that the proposed solution enhances the collaborative efficiency of production-logistics while ensuring the information security of the collaborating parties, effectively reducing production and delivery costs.

Rapid-response electrochromic devices with self-wrinkling polyaniline for enhanced infrared emissivity modulation

Electrochromic devices (ECDs) based on polyaniline (PANI) can alter their optical properties reversely by varying applied voltages, showing great potential in infrared (IR) information concealment. However, the traditional ECD with a flat PANI layer tends to perform unsatisfactorily in IR emissivity regulation and response time, resulting in risks of information leakage and low transfer efficiency. To address these challenges, a wrinkled microstructure is incorporated into the PANI layer (PANI-W) of the ECD (ECD-W) through a self-wrinkling method. Theoretic simulations combined with experimental results verify that the PANI-W endows an enhanced antireflection effect and a more complete redox degree. As a result, the ECD-W exhibits an exceptional IR emissivity modulation (0.375) and a short response time (1.8 s/2.6 s), allowing it to realize a temperature modulation of 8.9 °C. Moreover, various patterned and anti-patterned ECDs-W are feasibly prepared without masks, and more complicated patterns can be realized by erasing the unnecessary part of PANI-W on the ECDs-W. Two encryption/decryption strategies based on these ECDs-W are presented to provide more secure ways for IR information concealment. This research offers guidance to achieve high-performance ECDs with more effective spectrum regulation and faster responses, showing great potential in thermal regulation and IR information protection.

A secure, privacy-preserving, and cost-efficient decentralized cloud storage framework using blockchain

Cloud services benefit countless users worldwide due to notable features, such as on-demand self-service, scalability, easy maintenance, etc. Secure storage and access to data in the cloud is critical. Cloud Identity and Access Management (IAM) service, which acts in a centralized way to provide access requests to the authenticated users. Controlled access sometimes fails to preserve the privacy of the sensitive information stored in the cloud due to several reasons, such as insider attacks, breaches of data security, or any other types of unauthorized access. This paper suggests a blockchain-assisted secure storage and access mechanism to secure sensitive data. Here blockchain is used as a trust management entity that verifies the identity of the user. Along with this it issues the Access Control Lists (ACLs) and identity token, and at the same time, it records all the interactions between the users and service providers. Data transmission is transparent since transactions are recorded. Importance is given to user privacy and decryption keys security. Linear(t,n) secret sharing scheme is used for key share generation and distribution. For experimentation, in MetaMask cryptocurrency wallet Goerli test network is used. Results reveal that our model consumes less cost to execute than other existing works. The total execution cost to upload and download a data file is 0.00281392 and 0.02455307 GoerliETH. Where the all verification operations such as identity token, ACL, access_log, and data integrity are executed in Zero gas value. The proposed model maintains a constant gas cost regardless of transaction volume, with costs of 33.04 ETH and 32.24 ETH for data upload and download. Moreover, we present a comparison of execution time performance in three different system configurations.

Symbol-level scheme for combating eavesdropping: Symbol conversion and constellation adjustment

In the non-orthogonal multiple access scenario, users may suffer inter-multiuser eavesdropping due to the feature of successive interference cancellation, and the conditions of eavesdropping suppression methods in the traditional schemes may not be satisfied. To combat this eavesdropping, we consider physical layer security and propose a novel scheme by specially designing symbol conversion and constellation adjustment methods. Based on these methods, the amplitudes and phases of symbols are properly changed. When each user intercepts information as an eavesdropper, he/she has to accept high error probability, or he/she has to undergo exorbitant overhead. Analytical and numerical results demonstrate that the proposed scheme can protect the privacy of information, and this protection does not destruct the execution of successive interference cancellation and symbol transmission.

A privacy-preserving license plate encryption scheme based on an improved YOLOv8 image recognition algorithm

With the rapid development of green smart cities, urban intelligence has also brought new challenges, particularly in protecting the privacy information of vehicles within the city. To address this issue, we propose a novel image encryption scheme to ensure the security of image transmission. This method captures vehicle images through roadside surveillance cameras and uses an improved YOLOv8 algorithm to identify sensitive vehicle information in real time, which is then securely transmitted to the city’s traffic management system. To safeguard the data, we employ a combination of the Rabbit Competition scrambling algorithm and a custom diffusion kernel to perform real-time encryption on the identified image regions, protecting privacy-sensitive areas. Experimental results show that our method improves accuracy by 1.53% and average precision by 1.4% on the test set compared to the baseline model, indicating a substantial enhancement in detection accuracy. Additionally, the encryption scheme demonstrates a larger key space, improved robustness, and significantly enhanced anti-attack capabilities, confirming its effectiveness in protecting vehicle information in smart city environments.

Graduate Education in China Meets AI: Key Factors for Adopting AI-Generated Content Tools

Abstract Factors influencing Chinese graduate students’ adoption of AI-generated content (AIGC) tools are examined through partial least squares structural equation modeling (PLS-SEM) and fuzzy-set qualitative comparative analysis (fsQCA). The developed AIGCT-SI model incorporates key elements such as information accuracy, trust, and privacy concerns. PLS-SEM results indicate that performance expectancy, effort expectancy, facilitating conditions, and habit significantly impact students’ intentions, with trust acting as a key mediator, particularly for privacy concerns and social influence. FsQCA reveals seven configurations, demonstrating how combinations of performance expectancy, effort expectancy, and facilitating conditions drive adoption. A bidirectional relationship between privacy concerns and trust is observed, with trust mitigating privacy risks in several configurations. This integrative approach highlights the complex dynamics of AIGC tool adoption and provides strategic insights for their effective use in Chinese graduate education. As the findings are based on the Chinese context, further exploration in other educational settings is encouraged to validate their broader applicability.