Privacy Protection Issues Research Articles

A comprehensive outline towards conciliation in the contemporary world of E-commerce

Since the start of the millennial era, technical advancements have led to exponential growth in electronic commerce, or e-commerce. According to the United Nations Conference on Trade and Development (UNCTAD), e-commerce sales reached about US$25 trillion globally in 2021 [1] and are expected to grow at a compound annual growth rate of 9% through 2027, which is twice as fast as the 4% annual growth rate predicted for traditional in-person retail [2]. Online dispute resolution (ODR) techniques that aim to settle disagreements and lower the likelihood of expensive lawsuits are becoming more and more necessary as both individuals and corporations use online shopping platforms. Due of its legally binding nature, arbitration is the most formal method, as was briefly discussed [3]. Online arbitration is carried out using electronic methods, like email and digital tools designed to make the duties of arbitrators and parties to a dispute easier. It adheres to the same rules and processes as traditional arbitration. On the plus side of e-arbitration, advancements in technology allow for time and cost savings without sacrificing the ease of remote filing. The conclusion of e-arbitration agreements raises several negative issues, including privacy and data protection issues for the consumer signing the agreements and power imbalances between corporate entities and the consumer. The degree to which artificial intelligence (AI) is effectively and sufficiently analysing a dispute between parties is up for debate because it's unclear how new this adjudication process is.

Innovation and Development of Talent Management Model in Colleges and Universities Under the Background of Digitalization

The vigorous development of the digital economy has put forward new and higher requirements for talent training in colleges and universities. In order to cultivate outstanding talents in the new era with both interdisciplinary knowledge application ability and innovative thinking, colleges and universities must keep up with the pace of the development of the digital era, from macro to local, comprehensive innovation of talent management mode, and lead the development with innovation. Only in this way can they flexibly respond to various challenges and opportunities under the digital background, and help colleges and universities achieve the goal of long-term development. Based on the current situation of talent management in colleges and universities, this paper elaborates the feasible path of innovation and development of talent management mode in colleges and universities under the background of digitalization from the perspectives of talent training and talent service. At the same time, the exposed data security and privacy protection issues are also discussed in depth. To contribute to the realization of the goals of refinement, individuation, and intelligence of talent management in colleges and universities through the research and related exposition.

Testing the effects of personalized recommendation service, filter bubble and big data attitude on continued use of TikTok

PurposeThis study attempts to identify the determinant factors for Chinese users’ intention of continuous use of TikTok.Design/methodology/approachDrawing on the theoretical background and previous literature, this study proposes the research model followed by research hypotheses. For the purpose of empirical analysis of this study, we conducted a questionnaire survey on current Chinese TikTok platform users. Factor analysis was conducted to verify the reliability and validity of the measurement variables; Confirmatory factor analysis was conducted to ensure that the structural dimensions of the measurement are effective; the analysis results of the structural equation model for hypothesis test.FindingsThe result of the study indicates that the core service traits (entertainment, informativeness and convenience of use) of TikTok all have a positive effect on user engagement and perceived usefulness. In addition, the study found that both personalized recommendation services and filter bubble, which are features of TikTok's service recommendation system, have a positive effect on user engagement and perceived usefulness of TikTok. In addition, it was found that both user engagement and perceived usefulness have a significant positive effect on continuous use intention. Finally, users’ attitude toward big data indicating privacy concern only had a positive moderating effect on the personalized recommendation service but not on filter bubble.Originality/valueAs consumers’ demand for services tailored to their needs and preferences fast increased, it calls for growing concern over the safety of personal data being used by the platforms. Although this paradox over increased user convenience versus privacy protection issue has been persistently recognized, no previous research empirically addressed this issue in the context of social media platforms.

Extend UTAUT2 Model to Analyze User Behavior of China Construction Bank Mobile App

The outbreak of COVID-19 has been accelerating the digital transformation of financial institutions all over the world. Mobile banking APPs become an important channel to provide financial services in the Internet and Big Data era. Factors that restrict its development have attracted the close attention of scholars and practitioners around the world. Different from a general APP software, mobile banking APPs naturally have some financial attributes which set high requirements for personal privacy protection and other security issues. For this reason, this paper extends the original UTAUT2 model by incorporating a new constructor: perceived risks. The study carefully selects China Construction Bank’s mobile banking app (short as “the CCB APP”), as she is the biggest bank in China and has good novelty and representativeness in financial technology. This paper constructs an extended UTAUT2-based model using questionnaires and structural equation modeling the user behavior of CCB APP and tests the influencing factors of the proposed model through empirical analysis. Similar to other studies using the UTAUT2 model, the findings of this paper indicate that performance expectations, effort expectations, social influence, facilitators, and habits, all have a positive impact on CCB APP users’ intentions. Their intention to use was a mediating variable of user behavior, while facilitators directly influenced CCB users’ behavior. However, the study finds that perceived risk negatively affects the CCB users’ intention while hedonic motivation and price value do not significantly affect users’ decision-making process. Based on these findings, the misdirection should to be avoided in regard to financial APP development by financial institutions is discussed and the future research direction concerning mobile banking app adoption is illustrated.

Assessing the Recreational Fishers and their Catches based on Social Media Platforms: Privacy and Ethical Data Analysis Considerations

Social Media (SM) can offer insights to track recreational Fishing (Rf); Many Limitations Hinder Its Practical Application. The proportion and characteristics of RFs that share their catches remain unidentified. The objective was to enhance the surveillance abilities of RF by utilizing SM information. This study focuses on the sharing economy information transmission, network optimization of SM platforms, and privacy protection issues during data transmission. The study starts with the data transmission characteristics in SM platforms, analyzes the data ethics in SM information transmission from the perspectives of natural and economic data attributes, and then focuses on the privacy protection principles in network information transmission. Then, a privacy protection scheme based on a locally sensitive hash algorithm is constructed, and finally, an information transmission scheme and method optimization based on a network optimization module are proposed. The research gathered data using physical (face-to-face) surveys and digital (email) surveys to define marine RF that post catches on online platforms (“sharers”), together with additional demographic and fishing data. A comparative analysis was conducted on the computational convergence and accuracy of different privacy protection methods, and the optimal rule framework for data privacy protection was discussed. The observation results show that the efficiency of information transmission using the Minhash-SSNR method is slightly inferior to that using the OSNR-SSNR method. Minhash technology is based on a similarity comparison of dimensionality-reduced data. This leads to data causing the initially highly similar two sets of lists to be misjudged as not having enough similarity, thereby reducing some potential information dissemination opportunities and affecting the efficacy of information transmission. It can be seen that the Minhash-SSNR strategy can effectively send information to nodes with high similarity, preventing excessive information duplication within the system. Although the Minhash-SSNR strategy has a certain degree of decline in information transmission efficiency, it accounts for only about 5% compared to the OSNR-SSNR strategy, ensuring the essential operational stability of SM opportunity networks without significant impact. With few learning and training times, the network information transmission optimization module proposed in this study quickly achieved a lower exponential error. As the number of training sessions gradually increases, the exponential error of the network information transmission optimization module in this study is small, and the prediction accuracy of the method is high. RFs who captured a prize, iconic, or symbolic species tended to discuss their catches more. This research signifies significant progress in incorporating SM information to oversee RFs.

Chinas 5G Communication Technology Development Status and Countermeasures

With the acceleration of the global informatization process, 5G communication technology, as a new generation of mobile communication technology, with its advantages of high speed, low delay and large connection, has brought great development opportunities for all walks of life. This paper mainly analyzes the development status, challenges and countermeasures of Chinas 5G communication technology, and looks forward to the future development trend. In general, Chinas 5G communication technology has made remarkable achievements in policy support, infrastructure construction and technological innovation, but it still faces some challenges. In order to better promote the development of Chinas 5G communication technology, we need to increase policy support, strengthen industrial chain coordination, improve innovation capacity, improve regulatory policies and regulations, promote the integration of 5G technology and vertical industries, and pay attention to 5G security and privacy protection and other issues.

Keeping medical information safe and confidential: a qualitative study on perceptions of Israeli physicians

BackgroundPatients expect their information to remain confidential, and physicians have a legal and ethical obligation to keep it this way. Confidentiality is not just a legal requirement but a crucial element in establishing trust between patients and healthcare providers. Patients must feel confident that their personal and medical information is kept confidential and shared only with those who need to know. Previous studies have primarily concentrated on patients’ perceptions of medical confidentiality, data privacy, and data protection issues. However, research on the practical practices and perceptions of medical confidentiality among hospital physicians is scant, underscoring the need for a deeper understanding of this critical issue.MethodsThrough qualitative methods and as part of a large-scale study on aspects of patient dignity and responsiveness in Israel, physicians shared their views and practices on managing medical information.ResultsThe study revealed the practical challenges physicians face in upholding various aspects of data protection within hospital settings. These challenges, strategies, and deviations from data protection principles that physicians discussed are of significant practical relevance. The importance of patient consent and the practical measures for safeguarding patient information were also highlighted. While physicians acknowledged the importance of protecting patient information, they also grappled with the realities of doing so in a complex healthcare environment. In future healthcare policies, it is critical to ensure robust measures are in place to safeguard and uphold medical confidentiality. These can include specific measures to increase compliance, such as regularly monitoring compliance with confidentiality policies, producing safe and anonymous channels to voice concerns, and enforcing consequences for any breaches to ensure accountability.ConclusionsWhile protecting medical information has emerged as an important goal, it is equally crucial to strike a balance between the need to share information to advance and provide quality medical care. Physicians and policymakers must navigate this delicate balance. Additionally, organizations should strengthen compliance to enhance their monitoring and enforcement of confidentiality policies. Ineffective implementation of medical confidentiality leads to theoretical guidelines that do not translate effectively into practice.

Privacy Protection in Mobile Big Data: Challenges and Solutions

The pervasive use of mobile big data has profoundly altered daily life, providing unprecedented convenience and efficiency. However, with the proliferation of mobile devices and the explosive growth of data volume, the issue of user privacy protection has become increasingly severe. Location information and semantic information, as the two core components of mobile big data, can directly reflect users’ behavioral trajectories and thought dynamics, underscoring the importance of privacy protection. Although existing technologies can protect user data to a certain extent, traditional methods struggle to address increasingly sophisticated attack techniques in the face of evolving privacy threats. A comprehensive privacy protection scheme for mobile big data was proposed in this study, with a focus on two main areas: the privacy protection of location-based and semantic-based mobile big data. For location information protection, an uncertain graph model was employed to effectively resist combined attacks by jointly protecting the user layer and the location layer. For semantic information protection, a hypergraph clustering method was used to structurally protect the user layer and the semantic layer, enhancing the privacy security of semantic information. This study not only addresses existing gaps but also provides new solutions for mobile big data privacy protection, offering significant theoretical and practical value.

Application of Mobile Technology in Enterprise Management: From Mobile Office to Intelligent Decision Support

With the rapid advancement of information technology, the application of mobile technology in enterprise management has become increasingly widespread. From mobile office solutions to intelligent decision support systems, the proliferation of mobile technology has not only enhanced operational efficiency but also provided more flexible and convenient management methods. Existing research indicates that the adoption of mobile office solutions enables enterprises to overcome limitations of time and space, improving work efficiency and employee satisfaction. Additionally, the implementation of intelligent decision support systems allows enterprises to make faster and more accurate business decisions. However, current studies often focus on individual mobile technologies or application scenarios, lacking a systematic exploration of mobile office data sharing and intelligent decision-making collaboration. Moreover, most research overlooks privacy protection issues in the enterprise management decision-making process. To address these gaps, this paper proposes a scheme for mobile office data sharing and intelligent decision-making collaboration and examines the integration of privacy protection in the decision-making process. The findings not only enrich the theoretical framework of management studies but also provide enterprises with a systematic and practical solution for mobile technology applications, enhancing management efficiency and intelligence while effectively safeguarding data privacy and security.

Chatbots applications in civil aviation passenger service: A scoping review

With the continuous development and popularization of artificial intelligence technology, chatbots, as an innovative service tool, are gradually demonstrating their significant application potential in the field of civil aviation. Chatbots can engage in real-time interactions with passengers through natural language processing technology, providing personalized services. This scoping review aims to provide a preliminary analysis of the current status, advantages, challenges, and future prospects of chatbot applications in the field of civil aviation passenger services. This review was conducted in accordance with the PRISMA extension for scoping reviews guidelines. Studies were identified by searching five prominent databases - Scopus, ACM, IEEE Xplore, Springer Link, and Web of Science - covering the period from 2015 to 2024. Chatbots have demonstrated significant potential in various applications within civil aviation passenger service, including flight inquiries, ticket booking, customer support, and baggage tracking. Existing literature indicates that chatbots can significantly enhance passenger satisfaction and loyalty by providing personalized and convenient service experiences. Additionally, they can reduce operating costs and improve service efficiency for civil aviation participants. However, challenges such as technical limitations in natural language processing and privacy protection issues remain to be addressed.

Anti-Money Laundering (AML) Information Technology Strategies in Cross-Border Payment Systems

This paper aims to explore the application of information technology in the implementation of anti-money laundering (AML) measures within cross-border payment systems, analyzing how it enhances the compliance of financial institutions and strengthens the effectiveness of financial regulation. By examining the use of information technologies such as big data analytics, artificial intelligence, and blockchain in the field of AML, this paper reveals how these technologies assist financial institutions in more effectively identifying and preventing money laundering activities. Additionally, the paper discusses the challenges that may be encountered in the implementation of these technologies, such as data privacy protection, technical compatibility, and security issues, and proposes corresponding countermeasures and suggestions. The research findings indicate that, despite challenges, the application of information technology is of great significance for improving the AML compliance and regulatory efficiency of cross-border payment systems, providing new perspectives and solutions for financial regulatory authorities and institutions.

Efficient and Secure EMR Storage and Sharing Scheme Based on Hyperledger Fabric and IPFS

This study examines the issues of privacy protection, data security, and query efficiency in blockchain-based electronic medical record (EMR) sharing. It proposes a secure storage and sharing scheme for EMR based on Hyperledger Fabric and the InterPlanetary File System (IPFS). To mitigate the privacy risks of data mining that could reveal patient identities, we establish an attribution channel in Hyperledger Fabric to store EMR ownership information and a data channel to store the storage location, digest, and usage records of medical data. Encrypted medical data are stored in the IPFS. To improve query efficiency in the blockchain, we integrate queryable medical data attributes into a composite key for conditional queries, avoiding complex data filtering processes. Additionally, we use a zero-knowledge proof combined with smart contracts for decentralized identity verification, eliminating reliance on third-party centralized verification services and enhancing system security. We also integrate AES and proxy re-encryption techniques to ensure data security during sharing. This scheme provides a more secure, efficient, and privacy-preserving approach for EMR systems, with significant practical implications and broad application potential.

A Two-Stage Differential Privacy Scheme for Federated Learning Based on Edge Intelligence.

The issue of data privacy protection must be considered in distributed federated learning (FL) so as to ensure that sensitive information is not leaked. In this article, we propose a two-stage differential privacy (DP) framework for FL based on edge intelligence. Various levels of privacy preservation can be provided according to the degree of data sensitivity. In the first stage, the randomized response mechanism is used to perturb the original feature data by the user terminal for data desensitization, and the user can self-regulate the level of privacy preservation. In the second stage, noise is added to the local models by the edge server to further guarantee the privacy of the models. Finally, the model updates are aggregated in the cloud. In order to evaluate the performance of the proposed end-edge-cloud FL framework in terms of training accuracy and convergence, extensive experiments are conducted on a real electrocardiogram (ECG) signal dataset. Bi-directional long-short-term memory (BiLSTM) neural network is adopted to training classification model. The effect of different combinations of feature perturbation and noise addition on the model accuracy is analyzed depending on different privacy budgets and parameters. The experimental results demonstrate that the proposed privacy-preserving framework provides good accuracy and convergence while ensuring privacy.

PDPHE: Personal Data Protection for Trans-Border Transmission Based on Homomorphic Encryption

In the digital age, data transmission has become a key component of globalization and international cooperation. However, it faces several challenges in protecting the privacy and security of data, such as the risk of information disclosure on third-party platforms. Moreover, there are few solutions for personal data protection in cross-border transmission scenarios due to the difficulty of handling sensitive information between different countries and regions. In this paper, we propose an approach, personal data protection based on homomorphic encryption (PDPHE), to creatively apply the privacy computing technology homomorphic encryption (HE) to cross-border personal data protection. Specifically, PDPHE reconstructs the classical full homomorphic encryption (FHE) algorithm, DGHV, by adding support for multi-bit encryption and security level classification to ensure consistency with current data protection regulations. Then, PDPHE applies the reconstructed algorithm to the novel cross-border data protection scenario. To evaluate PDPHE in actual cross-border data transfer scenarios, we construct a prototype model based on PDPHE and manually construct a data corpus called PDPBench. Our evaluation results on PDPBench demonstrate that PDPHE cannot only effectively solve privacy protection issues in cross-border data transmission but also promote international data exchange and cooperation, bringing significant improvements for personal data protection during cross-border data sharing.

BPA: A Novel Blockchain-Based Privacy-Preserving Authentication Scheme for the Internet of Vehicles

The Internet of Vehicles (IoV) connects an isolated individual on the road to share information, which can improve traffic efficiency. However, the promotion of information sharing brings the critical security issues of identity authentication, followed by privacy protection issues in the authentication process in the IoV. In this study, we designed a blockchain-based conditional privacy-preserving authentication scheme for the IoV (BPA). Our scheme implements zero-knowledge proof (ZKP) to verify the identities of vehicles, which moves the authentication process down to the Roadside Units (RSUs) and achieves decentralized authentication at the edge nodes. Moreover, blockchain technology is utilized to synchronize a consistent ledger across all RSUs for recording and disseminating vehicle authentication states, which enhances the overall authentication process efficiency. We provide a theoretical analysis asserting that the BPA ensures enhanced security and effectively protects the privacy of all participating vehicles. Experimental evaluations confirm that our scheme outperforms existing solutions in terms of the computational and communication overhead.

Research and Practice of Online Postgraduate Quality Management Systems in Local Institutions with the Context of Digital Network

In the digital era, there is an urgent need to reconstruct the talent cultivation system with the support of computer technology, and it is crucial to create and build a quality control system for postgraduate education that meets the development of the times. The system is usually operated online using digital network technology. Most of the postgraduates admitted to the research group come from the same level of institutions or non-graduate training authorization units, and their scientific research ability is relatively weak, hence they need more guidance and help from the university and supervisors in the cultivation process. Relying on the university's "Intelligence +" academic characteristics, the group has explored and practiced digital transformation in terms of accurate publicity, quality improvement, and differentiation cultivation, which mainly includes the following two aspects. The first, take the online teaching management platform as the core, cooperate with the enrollment platform, the practice results platform, and the graduation work platform through the network, with the user experience of teachers and students as the test standard, mine data on the habits of teachers and students, realize the orderly operation of the data-driven postgraduate education process. The second is to improve the teaching management ability and innovation consciousness of teachers under the intelligence education conditions through data platform and online or underline staged training. At the same time, the Group will continue to pay attention to data security and privacy protection issues in the postgraduate QMS, and deeply explore the integration and application of big data technology and education management, so as to improve the management level of postgraduate education and adapt to the new situation and requirements of postgraduate education.

Application of cloud computing platform in industrial big data processing

With the rapid growth and increasing complexity of industrial big data, traditional data processing methods are facing many challenges. This article takes an in-depth look at the application of cloud computing technology in industrial big data processing and explores its potential impact on improving data processing efficiency, security, and cost-effectiveness. The article first reviews the basic principles and key characteristics of cloud computing technology, and then analyzes the characteristics and processing requirements of industrial big data. In particular, this study focuses on the application of cloud computing in real-time data processing, predictive maintenance, and optimization, and demonstrates its practical effects through case studies. At the same time, this article also discusses the main challenges encountered during the implementation process, such as data security, privacy protection, performance and scalability issues, and proposes corresponding solution strategies. Finally, this article looks forward to the future trends of the integration of cloud computing and industrial big data, as well as the application prospects of emerging technologies such as artificial intelligence and machine learning in this field. The results of this study not only provide practical guidance for cloud computing applications in the industry, but also provide a basis for further research in academia.

The Utilization of Homomorphic Encryption Technology Grounded on Artificial Intelligence for Privacy Preservation

The advent of Artificial Intelligence (AI) and Machine Learning (ML), particularly deep learning, has escalated the demand for computing resources. However, the high hardware requirements pose challenges for companies, compelling them to outsource ML tasks to the cloud. Nevertheless, concerns about cloud trustworthiness limit such applications. Encrypting data before uploading it to the cloud is a straightforward solution to ensure data security. However, traditional encryption schemes render ciphertext data unable to participate in operations within the ciphertext domain, posing challenges for data analysis. This paper delves into the pivotal role of homomorphic encryption in addressing the critical issue of privacy protection in machine learning.

Lightweight privacy-preserving authentication mechanism in 5G-enabled industrial cyber physical systems

With the deep integration of informatization and industrialization, cyber-physical system (CPS), which integrates computing, communication and control technologies, comes into being and has been widely used in industrial applications. A large number of information physical system devices and control systems are based on open internet connections, and security and privacy protection issues are gradually emerging, especially in the process of dynamic and difficult to control information physical system stream data transmission and storage, which will face new security threats. To alleviate these issues, user authentication mechanisms can prevent unauthorized access by adversaries in the CPS environment. For this purpose, we put forth a lightweight privacy-preserving authentication scheme for 5G-enabled ecosystems. This method utilizes edge devices to save online transaction data in network and securely transmits IoT devices data from the sensors to the edge gateway. Then, the edge gateway delivers and stores these data in the cloud. A detailed comparative study based on experimental results indicates that our proposal achieves a better balance between security and functionality features, communication and capitulation costs compared to other existing competitive solutions.

Research on machine learning technology with privacy protection strategy in recommendation field

Technologies such as machine learning can achieve accurate personalized recommendations. However, due to the collection and utilization of a large amount of user information in this process, people are widely worried about data security and privacy issues. This paper first introduces two key issues of privacy protection in the field of machine learning, namely data privacy and model privacy. On this basis, this paper introduces and analyzes homomorphic encryption, differential privacy and federated learning, and compares their advantages and disadvantages. Among them, homomorphic encryption technology has a large computational cost, differential privacy technology has a negative impact on system accuracy, and federated learning technology has a high training and communication cost. Therefore, it will be the future research direction to study more efficient and accurate recommendation models.