Search Results (57)

With the rapid development of information technology and the deep integration of the Internet platform, the scale and form of financial transactions continue to grow and expand, significantly improving users’ payment experience and life efficiency. However, financial transactions bring us convenience but also expose many security risks, such as money laundering activities, forged checks, and other financial fraud that occurs frequently, seriously threatening the stability and security of the financial system. Due to the imbalance between the proportion of normal and abnormal transactions in the data, most of the existing deep learning-based methods still have obvious deficiencies in learning small numbers sample classes, context modeling, and computational complexity control. To address these deficiencies, this paper proposes a symmetrical structure-based GAN-CNN model for lightweight financial fraud detection. The symmetrical structure can improve the feature extraction and fusion ability and enhance the model’s recognition effect for complex fraud patterns. Synthetic fraud samples are generated based on a GAN to alleviate category imbalance. Multi-scale convolution and attention mechanisms are designed to extract local and global transaction features, and adaptive aggregation and context encoding modules are introduced to improve computational efficiency. We conducted numerous replicate experiments on two public datasets, YelpChi and Amazon. The results showed that on the Amazon dataset with a 50% training ratio, compared with the CNN-GAN model, the accuracy of our model was improved by 1.64%, and the number of parameters was reduced by approximately 88.4%. Compared with the hybrid CNN-LSTM–attention model under the same setting, the accuracy was improved by 0.70%, and the number of parameters was reduced by approximately 87.6%. The symmetry-based lightweight architecture proposed in this work is novel in terms of structural design, and the experimental results show that it is both efficient and accurate in detecting imbalanced transactions. Full article

During the last decade, the blockchain landscape has rapidly evolved, fostering the development of decentralized applications (DApps) that utilize cross-chain interactions. Although existing technologies have enhanced transaction processing and introduced interoperability solutions, scalability challenges persist, undermining their effectiveness. In particular, traditional cross-chain DApp interaction protocols experience performance bottlenecks due to their dependence on on-chain validation mechanisms, resulting in increased latency and computational costs. To address these issues, this paper presents the ScaL2Chain protocol, which is designed to facilitate efficient and secure cross-chain transactions for DApps. ScaL2Chain leverages off-chain technologies, such as payment channels, to enable participants to conduct transactions with a minimal on-chain footprint. By implementing an innovative state verification mechanism, ScaL2Chain guarantees high performance, confidentiality, and transaction integrity. Our empirical evaluations indicate that ScaL2Chain significantly outperforms existing solutions in terms of transaction throughput. Specifically, compared to baseline systems, ScaL2Chain achieves a 7.9-times to 8.4-times improvement in permissionless environments and a 1.9-times to 35.8-times improvement in permissioned environments under workloads with 4-64 DApps and varying cross-chain transaction ratios (0–100%). Full article

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, making the development of post-quantum secure methods for privacy protection essential. This paper proposes a post-quantum ring signature scheme based on hash functions that can be applied to off-chain payments, enhancing both anonymity and linkability. The scheme is designed to resist quantum attacks through the use of hash-based signatures and to prevent double spending via its linkable properties. Furthermore, the paper introduces an improved Hash Time-Locked Contract (HTLC) that incorporates a Signature of Knowledge (SOK) to conceal the payment path and strengthen privacy protection. Security analysis and experimental evaluations demonstrate that the system strikes a favorable balance between privacy, computational efficiency, and security. Notably, the efficiency benefits of basic signature verification are particularly evident, offering new insights into privacy protection for post-quantum secure blockchain. Full article

This research investigates the role and effectiveness of Türkiye’s social security system and transfer expenditures in addressing poverty. Social security mechanisms are designed to alleviate poverty by helping individuals meet their essential needs. In Türkiye, transfer payments offer temporary relief, particularly to disadvantaged populations; however, they may also foster long-term dependency. Although the immediate impacts of such transfers are widely discussed in the academic literature, their contribution to alleviating structural poverty remains underexplored. This study focuses on how transfer expenditures influence individuals’ ability to achieve lasting well-being. The Turkish context is especially relevant due to recent institutional shifts in social security policy and the increasing politicization of social assistance. Employing data analysis at the regional level, this study assesses the effectiveness of social transfers and explores their structural role in poverty reduction. It also provides policy suggestions aimed at developing more inclusive and durable strategies. The results are intended to offer broadly applicable insights for other developing countries through the lens of Türkiye’s experience. Full article

Among searchable encryption techniques, multi-user dynamic searchable encryption (MUDSE) schemes are an important research direction. After the data owner transfers data to the cloud, it may be necessary to authorize different users to access some or all of the data while allowing for dynamic updates. Enabling dynamic data sharing in cloud storage while preserving users’ ability to search the data is crucial for promoting data flow and maximizing its value. This approach is particularly significant in addressing the data silo problem. However, existing security mechanisms remain imperfect, and most current scenarios assume that cloud servers are merely “curious but honest”. In reality, cloud servers may exhibit malicious behavior, such as returning incorrect or incomplete search results. Similarly, malicious users might falsify search results—for example, to avoid payment—or collude with cloud servers to steal other users’ search privacy. To address these challenges, this paper proposes an updatable multi-user dynamic searchable encryption scheme with bidirectional verification. The scheme enables secure dynamic data sharing in multi-user scenarios by constructing an index structure using homomorphic message authentication codes and bitmaps. This ensures secure updates to encrypted data without revealing the relationship between files and keyword search keys while providing forward and backward security. Regarding privilege management, the scheme employs updatable keys, ensuring that users can only generate valid search commands if they possess the latest encryption key. Additionally, blockchain technology is introduced to assist in verifying user honesty. Through actual testing and security analysis, the proposed solution demonstrates improved search speed over traditional methods while maintaining security. It also exhibits high adaptability for handling frequently changing cloud data. Full article

Digital agriculture has rapidly developed in the last decade in many countries where the share of agricultural production is a significant part of the total volume of gross production. Digital agroecosystems are developed using a variety of IT solutions, software and hardware tools, wired and wireless data transmission technologies, open source code, Open API, etc. A special place in agroecosystems is occupied by electronic payment technologies and blockchain technologies, which allow farmers and other agricultural enterprises to conduct commodity and monetary transactions with suppliers, creditors, and buyers of products. Such ecosystems contribute to the sustainable development of agriculture, agricultural engineering, and management of production and financial operations in the agricultural industry and related industries, as well as in other sectors of the economy of a number of countries. The introduction of crypto solutions in the agricultural sector is designed to create integrated platforms aimed at helping farmers manage supply lines or gain access to financial services. At the same time, there are risks of illegal use of computing power for cryptocurrency mining—cryptojacking. This article offers a thorough risk assessment of cryptojacking attacks on endpoint systems, focusing on identifying critical vulnerabilities within IT infrastructures and outlining practical preventive measures. The analysis examines key attack vectors—including compromised websites, infected applications, and supply chain infiltration—and explores how unauthorized cryptocurrency mining degrades system performance and endangers data security. The research methodology combines an evaluation of current cybersecurity trends, a review of specialized literature, and a controlled experiment simulating cryptojacking attacks. The findings highlight the importance of multi-layered protection mechanisms and ongoing system monitoring to detect malicious activities at an early stage. Full article

This paper presents a structured methodology for identifying and addressing key communication challenges in the forestry sector, using the Czech Republic (CZ) as a case study. The approach integrates the CIMO model, problem tree analysis, SWOT evaluation, and a confrontation matrix to identify critical factors influencing communication challenges and prioritize strategic responses. Findings from a representative public survey (n = 3600), participatory workshops, and expert consultations indicate that the primary challenge (C) in Czech forestry is a fragmented sector facing declining trust and minimal media presence. The proposed intervention (I) is a unified, strategic communication methodology designed to activate the mechanism (M) of shared narratives and coordinated messaging. This approach aims to achieve the desired outcomes (O): increased trust, engagement, and legitimacy. The absence of a unified, long-term communication strategy contributes significantly to public misunderstanding, erodes trust, and limits policy support. The study proposes evidence-based tools to enhance sector visibility, coherence, and public engagement. With direct payments for ecosystem services under debate in CZ, effective communication is a prerequisite for securing public and political support. The methods employed have broader relevance for forestry in other countries seeking strengthened stakeholder alignment through strategic communication. Full article

With the widespread use of credit cards in online and offline transactions, credit card fraud has become a significant challenge in the financial sector. The rapid advancement of payment technologies has led to increasingly sophisticated fraud techniques, necessitating more effective detection methods. While machine learning has been extensively applied in fraud detection, the application of deep learning methods remains relatively limited. Inspired by brain-like computing, this work employs the Continuous-Coupled Neural Network (CCNN) for credit card fraud detection. Unlike traditional neural networks, the CCNN enhances the representation of complex temporal and spatial patterns through continuous neuron activation and dynamic coupling mechanisms. Using the Kaggle Credit Card Fraud Detection (CCFD) dataset, we mitigate data imbalance via the Synthetic Minority Oversampling Technique (SMOTE) and transform sample feature vectors into matrices for training. Experimental results show that our method achieves an accuracy of 0.9998, precision of 0.9996, recall of 1.0000, and an F1-score of 0.9998, surpassing traditional machine learning models, which highlight CCNN’s potential to enhance the security and efficiency of fraud detection in the financial industry. Full article

Objectives: The objective of this study is to quantify healthcare resource utilization, economic burden, and the multi-level medical security system for Spinal Muscular Atrophy (SMA) patients in Shaanxi Province, China, from a societal perspective using a survey. Methods: This observational study employed an online survey with a retrospective cross-sectional design in Shaanxi Province, China. The survey examined various aspects of SMA, including resource utilization, direct and indirect economic burdens, and co-payment mechanisms within a multi-level medical security system. Results: Following the inclusion of nusinersen in the National Reimbursement Drug List (NRDL) in 2022, the treatment rate for SMA patients increased significantly. After risdiplam was added to the NRDL in 2023, its use also saw a marked increase. Treatment costs varied by SMA type: Type 1 incurred the highest costs (RMB 300,000 or USD 41,000), followed by Type 2 (RMB 270,000 or USD 37,000), Type 3 (RMB 200,000 or USD 27,000), and Type 4 (RMB 80,000 or USD 11,000). The primary sources of costs were productivity losses due to primary caregivers (32.94%), nusinersen usage (29.29%), and risdiplam usage (17.33%). Out-of-pocket costs for SMA patients accounted for 29.29% of the total costs. In 2023, basic medical insurance covered 49% of direct costs and 32% of total costs. Patients still had to pay 25.73% of the total cost for the direct costs. Conclusions: Basic medical insurance is a critical foundation for patient security and plays a pivotal role in reimbursement. In contrast, commercial insurance has a relatively limited impact on covering the costs for SMA patients. These findings highlight the substantial healthcare burden faced by SMA patients under the current healthcare system in China. Full article

Increasing adoption of electric vehicles (EVs) and the expansion of EV charging infrastructure present opportunities for enhancing sustainable transportation within smart cities. However, the interconnected nature of EV charging stations (EVCSs) exposes this infrastructure to various cyber threats, including false data injection, man-in-the-middle attacks, malware intrusions, and denial of service attacks. Financial attacks, such as false billing and theft of credit card information, also pose significant risks to EV users. In this work, we propose a Hyperledger Fabric-based blockchain network for EVCSs to mitigate these risks. The proposed blockchain network utilizes smart contracts to manage key processes such as authentication, charging session management, and payment verification in a secure and decentralized manner. By detecting and mitigating malicious data tampering or unauthorized access, the blockchain system enhances the resilience of EVCS networks. A comparative analysis of pre- and post-implementation of the proposed blockchain network demonstrates how it thwarts current cyberattacks in the EVCS infrastructure. Our analyses include performance metrics using the benchmark Hyperledger Caliper test, which shows the proposed solution’s low latency for real-time operations and scalability to accommodate the growth of EV infrastructure. Deployment of this blockchain-enhanced security mechanism will increase user trust and reliability in EVCS systems. Full article

Anonymous credential (AC) systems are privacy-preserving authentication mech-anisms that allow users to prove that they have valid credentials anonymously. These systems provide a powerful tool for several practical applications, such as anonymous pay-ment systems in e-commerce, preserving robust privacy protection for users. Most existing AC systems are constructed using traditional number-theoretic approaches, making them insecure under quantum attacks. With four decades of research in anonymous credential systems, there is a need for a comprehensive review that identifies the design structures of AC systems, organizes the research trends, and highlights unaddressed gaps for the future development of AC, especially bringing AC to post-quantum cryptography. This work is a complete study describing AC systems, as well as their architecture, components, security, and performance. Additionally, real-world implementations of various applications are identified, analyzed, and compared according to the design structure. Lastly, the challenges hindering the shift toward the quantumly secure lattice-based AC designs are discussed. Full article

Timely approvals and payments to the project participants are crucial for successful completion of construction projects. However, the construction industry faces persistent delays and non-payments to contractors. Despite the desirable benefits of automated payments and enhanced access to digitized data progress, most payment applications rely on centralized control mechanisms; inefficient procedures; and documentation that takes time to prepare, review, and approve. As such, there is a need for a reliable payment automation system that guarantees timely execution of payments upon the detection of completed works. Therefore, this study used a cutting-edge approach to automate construction payments by integrating blockchain-enabled smart contracts and scan-to-Building Information Modeling (BIM). In this approach, scan-to-BIM provides accurate, real-time building progress data, which serve as the source of verifiable off-chain data. A chain-link is then used to securely relay these data to the blockchain system. Blockchain-enabled smart contracts automate the execution of payments upon meeting contract conditions. The proposed approach was implemented on a real case study project. The actual site scan was captured using a photogrammetry 360° camera, which uses a combination of structured light and infrared depth sensing technology to capture 3D data and create detailed 3D models of spaces. This study leveraged accurate, real-time building progress data to automate payments using blockchain-enabled smart contracts upon work completion, thus reducing payment disputes by tying payments to verifiable construction progress, leading to faster release of payments. The findings show that this approach provides a transparent basis for payment, enhancing trust and allowing precise project progress tracking. Full article

The regulation of land use within territorial spaces has, to some extent, inhibited the free flow of land resources, giving rise to the dilemma of substantial losses and profits within and outside regulated areas. Investigating how to allocate “windfall profits” to compensate the regions that suffer windfall losses is of great importance for addressing regional development imbalances. This study, based on the perspective of restricted land development rights, employed an improved Cobb–Douglas (C-D) production function to analyze the differences in land input contributions between the agricultural and non-agricultural sectors. It quantified the extent of restricted land development rights, assessed their value using the opportunity cost method, and applied an economic adjustment coefficient to revise the regional fiscal transfer amounts. The results indicate the following: (1) The contribution of land factors to economic output is more significant in the non-agricultural sector than in the agricultural sector. (2) There are substantial differences in the quantities of restricted land development rights and their unit values across provinces and cities. Anhui Province has the largest restricted area, at approximately 71,945.52 hectares, while Guizhou Province has the smallest, at about 6452.62 hectares. Shanghai has the highest unit value, at around CNY 13.77 million per hectare, whereas Yunnan Province has the lowest, at approximately CNY 1.4748 million per hectare. (3) The total fiscal transfer amount for the provinces in the Yangtze River Economic Belt is about CNY 23.4 billion. Anhui Province receives the most compensation, at approximately CNY 8.5 billion, while Jiangsu Province has the highest expenditure, at about CNY 19.8 billion. Currently, the state should establish a comprehensive regional fiscal transfer compensation mechanism that takes into account the fiscal payment capacities of “windfall profit regions” and the incentive effects on “windfall loss regions” when determining fiscal transfer amounts. This approach aims to alleviate potential fiscal payment resistance in “windfall profit regions” and enhance proactive protection efforts in “windfall loss regions”, thereby achieving the coordinated development of economic growth, ecological improvement, and food security. Full article

The widespread reliance on paper-based currency poses significant drawbacks, such as counterfeiting, lack of transparency, and environmental impacts. While Central Bank Digital Currencies (CBDCs) address many of these issues, their dependence on continuous internet connectivity limits their usability in scenarios with poor or no network access. To overcome such limitations, this paper introduces ElasticPay, a novel Peer-to-Peer (P2P) Offline Digital Payment System that leverages advanced hardware security measures realised through Trusted Platform Modules (TPMs), Trusted Execution Environments (TEEs), and Secure Elements (SEs). ElasticPay ensures transaction privacy, unforgeability, and immediate settlement while preventing double spending. Our approach integrates robust recovery mechanisms and provides a scalable solution for diverse environments. Extensive experimentation validates the system’s reliability and practicality, highlighting its potential to advance secure and inclusive CBDC ecosystems. We demonstrate the proposed solution implementation on the iPhone mobilephone because it has an inbuilt Secure Enclave, which is an integrated implementation of the necessary TPM, TEE, and SE functionalities. Full article

As a new driving force for economic growth, the digital economy has had a profound impact on the labor market. While the existing research has explored the role of the digital economy in job substitution, creation, and polarization effects, the research on the impact on the social insurance fund income is relatively scarce. In view of this, based on the provincial panel data from 2011 to 2020, this paper analyzes the effect and mechanism of the digital economy on the pension income by using the moderated chain intermediary model and random forest regression. The results show that: (1) the employment scale, labor income, industrial structure, and government intervention are the important factors affecting the income of urban pension insurance; (2) the development of the digital economy has a negative impact on the income of the basic pension insurance fund for urban employees, and the chain intermediary effect that indirectly affects the employment scale and labor income through promoting the upgrading of the industrial structure has a negative impact on the income of the pension insurance fund. The employment scale and employment income of the industries with high and low substitution rates have a significant impact; (3) government intervention can regulate the negative impact of the digital economy development on the pension fund income. Furthermore, taking the transformation and reform of social security collection and payment institutions in July 2018 as an opportunity, the analysis using the event study method found that the average level of the pension income in the regions where the tax department was fully responsible increased significantly compared with the regions where the social security department collected it. Therefore, in order to maintain the sustainability of the pension fund income and effectively prevent the problem of old-age poverty caused by the “silver wave” and the lack of protection of workers’ rights and interests, institutional innovation should be promoted, the current tax policy should be adjusted, and the inclusiveness and flexibility of the pension security system should be improved. Digital technology should be used to improve the government’s intervention capacity and management level, and promote the positive interaction between the digital economy and the pension insurance system. Full article