Data Privacy Protection Research Articles

Secure data encryption key scenario for protecting private data security and privacy

Cryptography, specifically encryption, plays a pivotal role in protecting data from unauthorized access. However, not all encryption methods are equally effective, as some exhibit vulnerabilities. This research proposing a novel encryption method that builds upon established techniques to enhance data security. The proposed method combines the strengths of the Festial encryption method and the Advanced Encryption Standard (AES) to create an algorithm that exhibits superior resistance against attacks. The proposed encryption method successfully mitigates vulnerabilities, demonstrating enhanced resilience against unauthorized access attempts and minimizing the potential for data leakage. By prioritizing security and advancing encryption technologies, it can effectively protect personal information, maintain data confidentiality and integrity, and establish a safer digital environment for individuals and organizations.

Blockchain-Enhanced Data Privacy Protection and Secure Sharing Scheme for Healthcare IoT

Blockchain-Enhanced Data Privacy Protection and Secure Sharing Scheme for Healthcare IoT

Federation Chain for Data Privacy Protection in Industrial Internet of Things: The Perspective from 5G Core Networks

Federation Chain for Data Privacy Protection in Industrial Internet of Things: The Perspective from 5G Core Networks

Privacy-Preserving Multiview Matrix Factorization for Recommender Systems

With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies assume that the conventional FL framework can fully protect user privacy. However, there are serious privacy risks in matrix factorization in federated recommender systems based on our study. This paper first provides a rigorous theoretical analysis of the server reconstruction attack in four scenarios in federated recommender systems, followed by comprehensive experiments. The empirical results demonstrate that the FL server could infer users' information with accuracy <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;80\%$</tex-math></inline-formula> based on the uploaded gradients from FL nodes. The robustness analysis suggests that our reconstruction attack analysis outperforms the random guess by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$&gt;30\%$</tex-math></inline-formula> under Laplace noises with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$b\leq 0.5$</tex-math></inline-formula> for all scenarios. Then, the paper proposes a new privacy-preserving framework based on a threshold variant of homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems. The proposed PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.

Edge-Based Real-Time Sensor Data Processing for Anomaly Detection in Industrial IoT Applications

The Industrial Internet of Things (IIoT), which uses devices with sensors to provide real-time insights into crucial processes, has completely changed how industries function. However, there are many problems associated with the sheer volume and speed of data created in industrial environments, particularly when it comes to anomaly detection. The development of edge-based real-time sensor data processing techniques was required because traditional cloud-based solutions frequently experience latency problems and privacy issues. This study suggests a novel method for IIoT applications that focuses on processing sensor data at the edge, close to the data source, for anomaly identification. We offer real-time analysis of sensor data without the need for continuous data transfer to the cloud by utilising the processing capabilities of edge devices, such as industrial gateways and embedded systems. To find anomalies in streams of real-time sensor data, our methodology integrates data pre-processing, feature engineering, and machine learning algorithms. This strategy not only lessens the strain on the network's bandwidth but also ensures quick reaction to urgent situations, cutting downtime and boosting operational effectiveness. Proposed system has adaptive learning features that enable it to continuously adjust to altering ambient factors and sensor properties, enhancing the precision of anomaly detection over time. We provide experimental findings that show how our edge-based anomaly detection system performs well in diverse industrial situations. The results show that, while protecting data privacy and minimising latency, our methodology outperforms conventional cloud-based methods in terms of anomaly detection performance.

Real-Time Emotion Recognition using Deep Facial Expression Analysis on Mobile Devices

Numerous applications in human-computer interaction, healthcare, and other fields have been made possible by the growth of mobile devices, which has created new opportunities for real-time emotion recognition. This research introduces a novel method for mobile device-based deep facial expression analysis for real-time emotion recognition. In order to execute accurate and efficient emotion recognition directly on the device, our technology makes use of the computational capacity of contemporary smartphones. This eliminates the need for cloud-based processing and ensures user privacy. For mobile platforms, we use a deep learning architecture that is optimised for both speed and accuracy. A lightweight convolutional neural network (CNN) for facial feature extraction and a recurrent neural network (RNN) for temporal emotion modelling are the main elements of our system. Our system accurately detects and categorises a variety of emotions, including joy, sadness, rage, surprise, and more by processing video frames from the device's camera feed in real-time. We carried out extensive trials on a variety of datasets to assess our methodology, attaining state-of-the-art accuracy with minimal processing cost. Through a variety of applications, including emotion-aware virtual assistants, mental health tracking tools, and immersive gaming experiences, we show how useful our technology is. This paper makes a contribution to the burgeoning field of mobile-based emotion recognition by providing a strong and effective solution that enables researchers and developers to produce ground-breaking software that can better comprehend and react to human emotions while protecting data privacy and guaranteeing real-time performance on mobile devices.

Protecting users’ privacy in academic libraries in Egypt: a field study

The study aims to identify and analyse the policies for protecting the privacy of patrons’ data in Arabic and international academic libraries, study the legislation governing these policies. In order to achieve the objectives of the study, the descriptive analytical method has been used to assemble the policies of protecting data privacy of patrons of international academic libraries, and to study the current situation of academic libraries in Egypt. This is attained by using a set of data collection tools, the most important of which are the personal interviews, checklists, and questionnaires. The study concludes with a number of results, the most important of which are: illustrates the importance of using specialists in the field of libraries and information for their significance in raising awareness of the importance of protecting data privacy in academic libraries in Egyptian universities. Establishing policies to protect the privacy of beneficiary data in Egyptian academic libraries.

Digital Transformation in Politics and Governance in Indonesia: Opportunities and Challenges in the Era of Technological Disruption

Digital transformation has become a major driver of change in politics and governance in Indonesia. This study aims to examine the opportunities and challenges faced in the process of digitalizing the public sector in Indonesia. Using a qualitative approach with case studies and literature analysis, the research identifies several key aspects, including increased political participation through social media platforms, the evolution of e-governance, and the implications of cybersecurity and data privacy. The units of analysis include policies and practices of digitalization in the public sector. The study population consists of government agencies, active politicians on social media, and citizens participating in digital politics. A purposive sampling technique was used to select key government agencies, influential politicians on social media, and digitally active citizens. Data were collected through in-depth interviews and document analysis and analyzed using thematic analysis techniques. The findings indicate that digital technology creates new opportunities to enhance transparency, efficiency, and public participation in Indonesian governance. However, challenges such as the digital divide, cybersecurity threats, and data privacy protection remain. The results highlight the importance of adaptive policies and robust regulatory frameworks to maximize the benefits of digital transformation while addressing existing challenges.

Awareness of Personal Data Protection Law in concern to literacy

Understanding privacy literacy can help everyone treat their personal data shared on online platforms. Data protection starts from the idea that individuals have the right to information about themselves. There must be efforts to prevent misuse; hence, it makes digital literacy important. This research aimed to measure students' understanding of the importance of protecting personal data in the PDP Law based on the categories of knowledge, attitudes, and behavior. The research used quantitative methods with an analytical survey approach. Data analysis techniques used the Analytical Hierarchy Process (AHP). The population was 75 undergraduate and postgraduate students in West Java. The final score showed the number 81. Research results showed that the knowledge dimension scored 82, or a good level. It explained that most respondents understood data privacy protection laws. The attitude dimension had a score of 83, classified as good. Respondents were expected to stay informed about information protection and data privacy. The behavioral dimension was lower than the other dimensions, namely 77 or classified as an average level. In conclusion, the awareness level of the Data Privacy Protection Law based on the areas and dimensions that have been taken into account is at a good level, although it still needs improvement in terms of habits or behavior.

Maximum Load Consumption Capacity Maintenance of Distributed Storage Devices Based on Time-Varying Neurodynamic Algorithm

A charge and discharge management scheme is proposed. The stored electric energy in distributed storage devices will converge to the consistent. The consistency of the stored electric energy of the devices helps to maintain the maximum load capacity and maximum consumption capacity of distributed storage devices. The charging and discharging process is constructed as a time-varying optimization problem, and the proposed algorithm can respond to the time-varying parameters of the distributed storage devices in real time. The time-varying neurodynamic algorithms can obtain time-varying optimal solution trajectories to give the optimal charging and discharging strategy in real time. In addition, the proposed approach in this paper focuses on the privacy protection of device data. Each device can calculate the power of discharging or charging by communicating with the partially connected nodes. Numerical simulations of the proposed scheme in the paper are given to verify the effectiveness of the scheme. Numerical simulations show that our scheme can make the electric energy stored in each storage device converge and maintain the maximum load capacity or maximum consumption capacity of the whole distributed storage device.

Music teaching software development based on neural network algorithm and user analysis

With the continuous development of digital technology, music teaching is gradually shifting from traditional face-to-face teaching to online teaching and intelligent learning methods. Developing music teaching software plays an important role in improving users’ music learning methods and learning experience. However, current music teaching software still has issues such as poor compatibility, low level of data security and privacy protection, and functional requirements that are still in the early stages. This article aimed to research and develop a music teaching software based on neural network algorithm and user analysis, so as to improve user satisfaction with music teaching software. In the article, the overall design module of the system was first analyzed, and then music teaching resource data was collected and normalized using clustering algorithms to improve the effectiveness of music teaching resource data. Afterwards, a BP (Back Propagation) neural network model with parallel structure was used to optimize the design of the neural network model, and the effectiveness of the neural network model in user interface design was analyzed. Finally, user behavior analysis and personalized recommendation were achieved by constructing a user profile feature dataset. In order to verify the performance of the developed music teaching software, the software performance and security were tested. The results showed that the required loading time for the music score data in test case 1 of this article was only 1.21 s, and the response time was only 0.015 s. At this time, the security was 92.6%; the required loading time for audio data was only 1.06 s. The response time was only 0.019 s, and the security was 88.7%; the loading time required for user interaction data was only 0.78 s. The response time was only 0.012 s, and the safety was 91.3%. The research results indicated that the music teaching software developed in this article could better meet users’ music teaching needs and provide users with a more intelligent, personalized, and efficient music learning experience.

Artificial Intelligence and Its Impact to the Operations of Enterprises: Basis for Strategic Plan

Artificial intelligence has had a profound impact on the production and operation management of enterprises, including human resource management, financial management, production management, and decision-making assistance. Artificial intelligence can improve production and management efficiency, and reduce costs. It can improve the quality and specialization level of financial information processing, achieve intelligent financial management, and improve the efficiency and accuracy of financial management. The development of artificial intelligence may have an impact and crowding out effect on jobs with primary repetitive standardized processes, leading to a reduction and adjustment of some positions, while also providing new employment opportunities. The application of artificial intelligence in production and operation management can strengthen enterprise supply chain management, improve quality inspection efficiency, reduce operating costs, improve production efficiency, enhance the scientificity of enterprise management, thereby improving management efficiency and economic benefits, and promoting enterprise development and competitiveness. At the same time, it is necessary to pay attention to data security management and privacy protection, reduce the cost of using and maintaining artificial intelligence, continuously improve and perfect artificial intelligence tools, effectively enhance the use of artificial intelligence in accordance with regulations, and pay attention to the risk of personnel unemployment caused by the application of artificial intelligence. Therefore, it is suggested that senior leaders of enterprises should attach great importance to the application of artificial intelligence, increase funding, provide budget support and resource investment, strengthen data quality management and customized analysis capabilities, reduce usage and maintenance costs, promote human-machine cooperation, strengthen the application of artificial intelligence in business management, attach importance to employee training and transformation support, and pay attention to technological development trends, optimize and expand application scenarios, Actively explore and apply artificial intelligence tools to enhance the competitiveness of enterprises. The government should formulate relevant policies and regulations, strengthen education and training support, promote the development of the artificial intelligence industry, strengthen international cooperation and exchanges, formulate policies to promote employment and social security systems, encourage innovation and entrepreneurship, strengthen supervision and regulation, and promote the comprehensive application of artificial intelligence in enterprise management.

Digital Rights of Citizens in the Context of Public Administration of Social Security in Ukraine

The article deals with the issue of formulating the definition of the concept of “digital law” of citizens, reveals its essence, proposes a system of substantive components of “digital law” and substantiates recommendations for its consolidation in the Civil Code of Ukraine. It is determined that the content and essence of the concept of “digital law” should be understood as the right of individuals and legal entities to free access to the use of electronic devices and telecommunication networks intended for data transmission. The author substantiates the substantive components of the concept of “digital law”, namely, the right to free access to telecommunication networks for the transmission of publicly available information, the right to digital self-determination, the right to use electronic tools of democracy, and the right to be forgotten. It is emphasized that the legislative consolidation of the content of the concept of “digital law” and its components in the updated Civil Code of Ukraine should provide a regulatory framework for further development of secure cyberspace in the context of public administration of social security in Ukraine, which is becoming a priority for the modern state in times of war. The article also considers that an important right which, in our opinion, should be introduced into the Civil Code of Ukraine is the right to digital self-determination. This right is a new norm in national and international law and is determined by the circumstances of the growth in the number of public and social services provided on the Internet, with a certain curtailment of such services in the offline space. It is noted that in the context of public administration of social security in Ukraine, the concept of “digital law” should be understood as the rights of individuals and legal entities to free access to the use of electronic devices and telecommunication networks intended for data transmission. The following rights should be officially recognized as substantive components of the concept of “digital law”: the right to free access to telecommunication networks for the transmission of publicly available (unprohibited) information (access to the Internet), the right to digital self-determination, the right to use electronic tools of democracy, the right to protection of private data and the right to be forgotten.

Research on the Marketing Transformation Path of Household Appliance Enterprises under the Background of Artificial Intelligence

The article first explores how AI can fundamentally change the practices of these companies in terms of brand image, product design, and marketing efficiency. AI can help companies understand the preferences and behaviors of target markets through data-driven insights, which can be used to create personalized marketing messages, and it allows companies to consider various factors, all of which can be incorporated into the product development process, ensuring that the final product not only meets market demand but also exceeds customer expectations, thus marketing efficiency can be significantly improved through AI. However, the rapid development of AI technology has also brought a series of challenges to home appliance enterprises. The most important issues are data security and privacy protection, another challenge is the reliability and fairness of AI technology. Therefore, home appliance companies must consider the ethical impact of their AI systems and strive to ensure fairness and transparency. Given these challenges and opportunities, it is evident that home appliance companies need to skillfully harness AI technology to achieve a higher level of personalized marketing transformation.

A Matrix Coding-Oriented Reversible Data Hiding Scheme Using Dual Digital Images

With the development of Internet technology, information security and data protection have become particularly important. Reversible data hiding is an effective technique for data integrity and privacy protection, and secret image sharing is a distinct research field within reversible data hiding. Due to the ability of sharing secret information between two receivers and the larger embedding capacity compared to the traditional reversible data hiding scheme, dual digital images have also attracted extensive research in the past decade. In this paper, we propose a reversible data hiding scheme based on matrix coding using dual digital images. By modifying the bits in the pixels, we can conceal three bits of the secret message in two pixels. In other words, the embedding rate reaches 1.5 bits per pixel (bpp). The experimental results demonstrate that our method has a significantly larger embedding capacity of 786,432 bits compared to previous similar methods while still maintaining acceptable image quality defined by a peak signal-to-noise ratio (PSNR) greater than 30 dB. The proposed scheme is suitable for applications required to pass a large amount of data but with minor security of image quality to be visually acceptable.

Challenges and Opportunities of Big Data Analytics for Human Resource Management in Mining and Metal Industries

Big data analytics has been transforming various industries and sectors including mining, metals, metallurgical, etc., and the mining sector, known for its complex and dynamic workforce requirements, is increasingly turning to Big Data Analytics to optimize its Human Resource Management (HRM) practices. The mining sector, being a significant economic segment, contributes to economic growth and is confronted with the dual challenges of a rapidly aging workforce and a shortage of skilled talent. As the mining industry is facing a talent crunch, HR has a significant role in attracting and retaining new talent in this industry, as well as ensuring that competent talent is brought in to meet the sector’s future demands Big data analytics for HR refers to the use of data analysis techniques to gain insights into employee behavior, engagement, productivity, and retention. The purpose of this paper is to explore the challenges and opportunities of big data analytics for HR, best practices for implementing it, and case studies of companies that have successfully implemented big data analytics for HR. The paper begins by outlining the importance of big data analytics for HR, highlighting its ability to provide HR professionals with a better understanding of employee behavior, engagement, and motivation. The challenges of big data analytics for HR are then discussed, including data quality and accessibility, integration of data from various sources, protection of data privacy, lack of expertise and resources, and resistance to change and adoption. The opportunities of big data analytics for HR are then presented, including predictive analytics for talent management, data-driven recruitment and selection, employee engagement and retention, performance management and productivity, and diversity, equity, and inclusion. Best practices for implementing big data analytics for HR are also discussed, including defining clear objectives and metrics, selecting the right tools and technologies, building a data-driven culture, collaborating with IT and other departments, and ensuring data privacy and security. Case studies of successful big data analytics in HR are presented to provide real-world examples of companies that have leveraged big data analytics to improve their HR functions. Examples include IBM's talent analytics program, Google’s use of big data analytics to identify key drivers of employee satisfaction and engagement, and leading mining company, referred to as “MinerCo,” leveraged Big Data Analytics to address HRM challenges and harness opportunities for improvement.

A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: Problems, challenges and solutions

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, proof-of-authority (PoA)-based blockchain distributed ledger technology (DLT) has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provides recommendations that could facilitate the continuous growth of blockchain applications in healthcare. Lastly, the study explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.

Protection path of personal data and privacy in China: Moving from monism to dualism in civil law and then in criminal law

The relationship between privacy protection and personal data protection is legally and academically challenging. The PRC's civil law has evolved from the traditional monism of privacy right provision (protecting personal information) to the dualism of separating privacy protection and personal information protection. But when the private information within the privacy sphere involves personal information, the dualism protection is not absolute, but more delicate than it has been suggested.More importantly, there is little in the existing literature analysing the noteworthy divergence of the protection path of privacy and personal information in civil law and criminal law – or considering the reasonable convergence of the protection path under the civil and criminal law. China's Criminal Law has sought to protect privacy with personal information provision for more than a decade. But it is presently difficult to convert privacy content into private information for personal information protection purposes. Given the multifaceted forms of contemporary privacy invasion and comparative law trends and thoughts, we seek to build a protection framework for privacy in the information age under the criminal law – and continually updating it – so as to make use of the expression function of coherence law theory to nurture a culture of respecting individual privacy and trusting legal protection in the information technology environment. The eventual convergence of dualism protection of personal data and privacy in both civil law and criminal law corresponds to the doctrinal coherence in the information age and to policy considerations in the digital economy and society. Privacy is by default an inalienable right to protect dignity and liberty – while personal information can be a comprehensive interest, which values and balances security and purpose, in accordance with law, in a specific jurisdiction such as contemporary China. The dualism exemplifies criminal law's coordination with civil law in protection.

CFSL: A Credible Federated Self-Learning Framework

Federated learning can collaboratively train AI models while protecting data privacy. In practical industry environment, Non-IID characteristics of data affect the effectiveness of federated learning. Personalized federated learning can help resolve this, but it cannot adapt to unknown data. In addition, practical applications also call for trusted training environment and remain stable when there are security threats. In this paper, we propose a Credible Federated Self-Learning (CFSL), based on the idea of hypernetwork supported by blockchain to achieve secured, credible, personalized federated self-learning, especially, for unknown data in Non-IID environment. Extensive experiments on three Non-IID datasets demonstrate the capabilities on adaptive resilience for security attacks and on accuracy of recognizing unknown objects, with good performance at the same time. CFSL outperforms the existing personalized federated learning methods, with an increase in average accuracy by 4.11%.

RETRACTED ARTICLE: Research on key technologies of data security and privacy protection in Internet of Things group intelligence

RETRACTED ARTICLE: Research on key technologies of data security and privacy protection in Internet of Things group intelligence