User Data Security Research Articles

Exploring the Feasibility and Acceptability of Technological Interventions to Prevent Adolescents' Exposure to Online Pornography: Qualitative Research.

Amid growing concern over children's access to online pornography, policy makers are looking toward new and emerging technological concepts for unexplored solutions including artificial intelligence and facial recognition. This study sought to explore and ideate emerging technological interventions that are feasible, acceptable, and effective in preventing and controlling the exposure of young people to online pornographic material. We conducted a series of qualitative co-design workshops with both adult (n=8; aged 32-53 years) and adolescent participants (n=4; aged 15-17 years) to ideate potential technological interventions that are feasible, acceptable, and effective at preventing and controlling the exposure of young people to online pornographic material. A story stem methodology was used to explore participants' attitudes toward two unique technological prototypes. Participants expressed a generally favorable view of the proposed technological concepts but remained unconvinced of their overall utility and effectiveness in preventing the intentional viewing of pornography by young people. Age-appropriate parent-child conversations remained participants' preferred approach to mitigating potential harms from pornographic material, with parents also expressing a desire for more educational resources to help them better navigate these discussions. User privacy and data security were a primary concern for participants, particularly surrounding the use and collection of biometric data. Internationally, policy makers are taking action to use age assurance technologies to prevent children's access to online pornography. It is important to consider the needs and opinions of parents and young people in the use and implementation of these technologies. Participants in this study were generally supportive of new and emerging technologies as useful tools in preventing the accidental exposure of young people to online pornographic material. However, participants remained less convinced of their ability to avert intentional viewing, with substantial concerns regarding technological efficacy, adaptability, and user privacy. Further, co-design and prototype refinement are needed to better understand user acceptability and comfortability of these new technological interventions, alongside additional research exploring sociocultural differences in information needs and user experiences.

Research on Driver Emotion Monitoring and Regulation System in Intelligent Assisted Driving

Abstract. With the rapid development of smart connected vehicles, the influence, recognition and regulation of driver's emotions are crucial for intelligent cockpits with "emotional interaction". This review aims to provide a comprehensive analysis of driver emotion monitoring systems in intelligent assisted driving. By summarizing and analyzing related literature, the structure and application of emotion monitoring systems are introduced, their principles and evaluation criteria are discussed in depth, and the advantages and limitations of the three sub-systems, namely, detection, decision-making and regulation, are analyzed in detail. The results show that although emotion monitoring and modulation technologies perform well in enhancing driving safety and comfort, their practical applications still face challenges in terms of technology maturity, cost control, and privacy protection. In addition, the reliability and accuracy of current emotion monitoring systems in complex driving environments have yet to be improved. Future research should focus on interdisciplinary cooperation to integrate the latest achievements in psychology, computer science and engineering. At the same time, it is necessary to develop unified technical standards and evaluation guidelines to promote compatibility and interoperability between different systems and enhance research on privacy protection measures to ensure the security and confidentiality of user data. Through these efforts, the field of intelligent driving is expected to achieve comprehensive development and wide application, further enhancing driver safety and user experience.

Cloud-Based Blockchain for Secure Data Sharing and Collaboration

Abstract: This study seeks to delve into the interrelation between user training and data security events in light of cloud-based blockchain technology improvement in terms of data security in collaborative contexts. A total of five firms was taken to collect data from a quantitative study, focusing more on variables that surrounded user training levels as well as data security events. This reflects that there exists a strong statistical negative correlation of frequency of security incidents and user training. Hence it has been established that the increase in the extent of training has considerably reduced vulnerabilities. Moreover, it was observed that there exists a positive relation between user participation and training. This means that to safe-guard data shared procedures, successful training activities can play an essential role. Such findings may be practically useful for firms who wish to take maximum advantage of their data security strategy.

The analysis of partial consent implementation effectiveness for terms & conditions on data awareness and data security of social media application users

Terms and Conditions are always encountered when using social media applications to determine which data can be accessed and what cannot. However, there are shortcomings in their implementation and communication, often causing users to be unwilling to read them. Therefore, this study aimed to analyze the effectiveness of implementing partial consent in Terms and Conditions concerning user Data Awareness and Data Security in social media. This Paper administered a questionnaire, distributed with a form, to students who use social media to understand their opinions regarding the partial consent concept. This paper analyzed the data using descriptive statistical methods. The results show a positive response from respondents towards implementing the partial consent concept, the users feel the terms and conditions are more effective in increasing user data awareness and security.

Perbandingan Evaluasi Kerentanan Menggunakan Tenable Nessus Scanner dan Owasp Zed Attack Proxy untuk Meningkatkan Keamanan Sistem Informasi Kepegawaian di Universitas Merdeka Malang

This study aims to compare the vulnerability analysis between Tenable Nessus Scanner and OWASP Zed Attack Proxy (ZAP) for improving the security of the Human Resource Information System (HRIS) website at Universitas Merdeka Malang. The research methodology includes the use of both Nessus and OWASP ZAP tools to scan the HRIS website for potential vulnerabilities. The findings of this research indicate that OWASP ZAP identified several critical web application vulnerabilities such as the absence of Anti-CSRF tokens, lack of Content Security Policy (CSP) headers, and missing Anti-Clickjacking headers, which are essential for maintaining the security and integrity of user data. On the other hand, Nessus Scanner focused more on network and server infrastructure vulnerabilities. The results suggest that OWASP ZAP is more effective for web application security in this context. Recommendations are provided to address the identified vulnerabilities and enhance the overall security of the HRIS website.

Adoption of smart energy apps and household energy practices: empirical insights from Norway

ABSTRACT This study examines the adoption and usage patterns of household energy apps among a sample of Norwegian households to explore their potential in visualising and controlling home energy consumption and influencing sustainable energy practices. Through a questionnaire survey of 150 respondents using energy apps, this study gathers empirical insights into adoption motivations, user perceptions and factors associated with self-reported changes in household energy practices. Findings indicate that app-related factors, such as perceived app insights applicability, ease of use, and appealing user interface, are more positively associated with self-reported changes in daily energy habits than demographic factors. This research underscores the importance of prioritising user experience and data security in app development to foster sustainable energy practices. It also offers practical recommendations for targeted engagement strategies and policy interventions to promote the wider adoption of smart energy apps and maximise their potential to facilitate household energy transitions.

Legal Perspective on Consumer Personal Data Protection in Fintech Services

The development of science and technology has significantly affected the financial transaction system in Indonesia, especially with the emergence of the Fintech business model. The rapid growth of Fintech indicates the need for smart management in order to provide optimal benefits to society. However, the potential misuse of consumers' personal data poses a risk that needs to be considered. This research uses a normative juridical and analytical descriptive approach to understand the legal dynamics in the Fintech industry and legal measures aimed at Safeguarding user data security in Indonesia, in accordance with the provisions of Financial Services Authority Regulation Number 13/Pojk.02/2018. The results show that Fintech business organizers and users have a relationship that is regulated through an electronic agreement. The legal safeguarding of personal data for Fintech user is governed by Financial Services Authority Regulation Number 1/Pojk.07/2013 and other pertinent regulations, addressing aspects such as confidentiality, data security, and the governance and risk management of information technology.

Face Recognition-Based Door Lock Security System Using TensorFlow Lite

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies

DB-FL: DAG blockchain-enabled generalized federated dropout learning

To protect user privacy and data security, the integration of Federated Learning (FL) and blockchain has become an emerging research hotspot. However, the limited throughput and high communication complexity of traditional blockchains limit their application in large-scale FL tasks, and the synchronous traditional FL will also reduce the training efficiency. To address these issues, in this paper, we propose a Directed Acyclic Graph (DAG) blockchain-enabled generalized Federated Dropout (FD) learning strategy, which could improve the efficiency of FL while ensuring the model generalization. Specifically, the DAG maintained by multiple edge servers will guarantee the security and traceability of the data, and the Reputation-based Tips Selection Algorithm (RTSA) is proposed to reduce the blockchain consensus delay. Second, the semi-asynchronous training among Intelligent Devices (IDs) is adopted to improve the training efficiency, and a reputation-based FD technology is proposed to prevent overfitting of the model. In addition, a Hybrid Optimal Resource Allocation (HORA) algorithm is introduced to minimize the network delay. Finally, simulation results demonstrate the effectiveness and superiority of the proposed algorithms.

Design of Intelligent Algorithm for Object Search Based on IoT Digital Images

With the development of artificial intelligence, traditional object search and image recognition have been replaced by the Internet of Things and artificial intelligence. However, traditional object search algorithms often lack accuracy and low precision. Therefore, this study proposes a new intelligent encryption algorithm to address the issues of insufficient accuracy in object search algorithms and image recognition algorithms. The new algorithm ensures the security of user data and the response efficiency of the model during the conversation process by integrating fully homomorphic encryption technology and dynamic sparse attention mechanism. The dynamic sparse attention mechanism introduced simultaneously improves the model's ability to handle long sequence data by dynamically adjusting attention weights. Experimental results showed that the precision of the proposed algorithm was 0.05% higher than that of random algorithms and 0.19% higher than that of sorting algorithms. The recall rate of the proposed algorithm was 0.14% higher than that of random algorithms and 0.16% higher than that of sorting algorithms. The research algorithm can identify objects with certain characteristics and is suitable for specific environments, greatly reducing the probability of data leakage in object search and providing new ideas for research in this field.

Analysis of authentication methods for full-stack applications and implementation of a web application with an integrated authentication system

The subject of research is methods and techniques for secure user authentication in web applications. The goal of the work is to analyse authentication methods and implement a web application with an authentication system integrating JWT tokens and the OAuth v2.0 standard. The article solves the tasks of analysis of the main protocols and methods of user authentication in web applications, implementation of authentication based on the OAuth 2.0 standard and JWT access/refresh token, and analysis of the risks of vulnerabilities and attacks for the implemented web applications. Methods used: comparison, empirical analysis, calculation methods. The next results have been obtained: analysed the protocols and methods of user authentication in web applications; selected authentication methods of JWT token and OAuth v2.0 standard for building modern web applications; created a web application based on the selected authentication methods in web applications; analysed the risks of vulnerabilities and attacks in web applications. Conclusions: The most well-known authentication methods for web applications are analyzed. It is established that most modern authentication methods have many disadvantages, which leads to increased risks when using these authentication methods. It is shown that one of the most reliable methods of web application user data security is the use of a combination of JWT Access/Refresh token and browser fingerprints. The implementation, configuration, and analysis of this methodology have shown that this combination provides the most reliable prevention of token theft and use from another computer. OAuth 2.0 authentication was also implemented. The study found that delegating authentication to services such as Facebook or Google can provide a low risk of attacks and vulnerabilities for a web application. It is noted that authentication using OAuth 2.0 can be compromised only at the beginning of the connection between the client and the server, or rather when the client first sends initial information from the browser fingerprints. This information is sent over the secure HTTPS (Hypertext Transfer Protocol Secure) protocol, so the risk of compromising OAuth 2.0 authentication is low.

Cybersecurity in mobile fintech applications: Addressing the unique challenges of securing user data

With the rapid proliferation of mobile fintech applications, the financial industry has witnessed significant transformations in how consumers manage, transfer, and invest money. However, the increasing reliance on mobile platforms has also introduced unique cybersecurity challenges. This review paper examines the specific threats facing mobile fintech applications, evaluates current security measures, and discusses future directions to enhance user data protection. Through an analysis of recent literature, this paper aims to provide a comprehensive overview of the state of cybersecurity in mobile fintech and the ongoing efforts to secure sensitive financial information.

DefendFL: A Privacy-Preserving Federated Learning Scheme Against Poisoning Attacks.

Federated learning (FL) has become a popular mode of learning, allowing model training without the need to share data. Unfortunately, it remains vulnerable to privacy leakage and poisoning attacks, which compromise user data security and degrade model quality. Therefore, numerous privacy-preserving frameworks have been proposed, among which mask-based framework has certain advantages in terms of efficiency and functionality. However, it is more susceptible to poisoning attacks from malicious users, and current works lack practical means to detect such attacks within this framework. To overcome this challenge, we present DefendFL, an efficient, privacy-preserving, and poisoning-detectable mask-based FL scheme. We first leverage collinearity mask to protect users' gradient privacy. Then, cosine similarity is utilized to detect masked gradients to identify poisonous gradients. Meanwhile, a verification mechanism is designed to detect the mask, ensuring the mask's validity in aggregation and preventing poisoning attacks by intentionally changing the mask. Finally, we resist poisoning attacks by removing malicious gradients or lowering their weights in aggregation. Through security analysis and experimental evaluation, DefendFL can effectively detect and mitigate poisoning attacks while outperforming existing privacy-preserving detection works in efficiency.

Current Trends on Advancement in Smart Textile Device Engineering

AbstractSmart textiles represent a revolutionary approach to wearable technology with applications ranging from healthcare to energy harvesting. This review paper explores the importance of textile technologies and highlights their potential to revolutionize consumer electronics. Conventional technologies are sometimes heavy, and lack comfort and flexibility, but smart textiles seamlessly integrate into everyday clothing, improving wearability and user experience. The article emphasizes the need for sustainable sourcing and environmentally friendly production methods, as well as responsible manufacturing and disposal practices. Manufacturing techniques such as wet spinning, melt spinning, electrostatic spinning, weaving, knitting, and printing are detailed and shed light on their role in incorporating electronics into textiles. Several applications of textile‐based devices are being explored, including biochemical sensing, temperature monitoring, energy harvesting, energy storage, and smart displays. Each application demonstrates the versatility and potential of smart textiles in different areas. Despite optimistic progress, challenges remain, from improving energy efficiency to protecting user privacy and data security. The review analyzes these problems and suggests future improvements, including interdisciplinary collaboration to find new solutions. Finally, an overview of the current state of smart textiles provides the future of this technology. It serves as an in‐depth reference for academics and readers interested in understanding recent advances and discoveries in textile technologies, highlighting the importance of this rapidly growing industry.

An Effective Federated Object Detection Framework with Dynamic Differential Privacy

The proliferation of data across multiple domains necessitates the adoption of machine learning models that respect user privacy and data security, particularly in sensitive scenarios like surveillance and medical imaging. Federated learning (FL) offers a promising solution by decentralizing the learning process, allowing multiple participants to collaboratively train a model without sharing their data. However, when applied to complex tasks such as object detection, standard FL frameworks can fall short in balancing the dual demands of high accuracy and stringent privacy. This paper introduces a sophisticated federated object detection framework that incorporates advanced differential privacy mechanisms to enhance privacy protection. Our framework is designed to work effectively across heterogeneous and potentially large-scale datasets, characteristic of real-world environments. It integrates a novel adaptive differential privacy model that strategically adjusts the noise scale during the training process based on the sensitivity of the features being learned and the progression of the model’s accuracy. We present a detailed methodology that includes a privacy budget management system, which optimally allocates and tracks privacy expenditure throughout training cycles. Additionally, our approach employs a hybrid model aggregation technique that not only ensures robust privacy guarantees but also mitigates the degradation of object detection performance typically associated with DP. The effectiveness of our framework is demonstrated through extensive experiments on multiple benchmark datasets, including COCO and PASCAL VOC. Our results show that our framework not only adheres to strict DP standards but also achieves near-state-of-the-art object detection performance, underscoring its practical applicability. For example, in some settings, our method can lower the privacy success rate by 40% while maintaining high model accuracy. This study makes significant strides in advancing the field of privacy-preserving machine learning, especially in applications where user privacy cannot be compromised. The proposed framework sets a new benchmark for implementing federated learning in complex, privacy-sensitive tasks and opens avenues for future research in secure, decentralized machine learning technologies.

Effective Application of Computer Network Security Technology in E-commerce Operation and Maintenance

As an important part of the modern economy, the security of e-commerce operation and maintenance directly affects the business continuity of enterprises and the trust of users. This paper explores the application of computer network security technology in operation and maintenance, analyzes existing technologies and actual cases, evaluates its effects, and looks forward to future development trends. The paper shows that the use of advanced network security technology can significantly improve the security and reliability of e-commerce systems.IntroductionIt has developed rapidly around the world and has become an important way for people to shop and for enterprises to operate e-commerce. However, the booming development of e-commerce also inevitably brings serious security threats, including data leakage, hacker attacks, and malware. In order to ensure the normal operation of e-commerce systems and the security of user data, the application of computer network security technology is extremely important. This paper aims to systematically explore the actual application effects of these technologies in e-commerce and provide reference for future research and practice.

Effective Application of Computer Network Security Technology in E-commerce Operation and Maintenance

As an important part of the modern economy, the security of e-commerce operation and maintenance directly affects the business continuity of enterprises and the trust of users. This paper explores the application of computer network security technology in operation and maintenance, analyzes existing technologies and actual cases, evaluates its effects, and looks forward to future development trends. The paper shows that the use of advanced network security technology can significantly improve the security and reliability of e-commerce systems.IntroductionIt has developed rapidly around the world and has become an important way for people to shop and for enterprises to operate e-commerce. However, the booming development of e-commerce also inevitably brings serious security threats, including data leakage, hacker attacks, and malware. In order to ensure the normal operation of e-commerce systems and the security of user data, the application of computer network security technology is extremely important. This paper aims to systematically explore the actual application effects of these technologies in e-commerce and provide reference for future research and practice.

A hybrid encryption approach for efficient and secure data transmission in IoT devices

Security is a crucial concern in the Internet of Things (IoT) ecosystem. Due to IoT devices' constrained processing and storage resources, providing reliable security solutions is challenging. Encryption is one of the most commonly used techniques to secure user data against unauthorized access. Therefore, it is essential to develop encryption solutions that have minimal impact on the performance of IoT devices. This study introduces a hybrid encryption approach that combines symmetric blowfish encryption with asymmetric elliptic curves. Blowfish encryption is used to encrypt large volumes of data, which could otherwise affect the execution time.In contrast, elliptic curve cryptography is utilized to ensure the security of the private key, which has a small size and does not increase the execution time significantly. The suggested approach provides advantages of both asymmetric and symmetric encryption methods, leading to an improvement in throughput and a reduction in execution time. The proposed approach was evaluated, yielding promising results in comparison to other cryptographic algorithms. The results show the optimization of more than 15% in the execution time and the efficiency increase by the proposed solution. This improvement represents security with the least impact on processing resources.

Makemates – Social Media Web Application

"Makemates" is a cutting-edge social media application designed to cater to the diverse needs and desires of modern-day social networking. This all-in-one platform offers a comprehensive suite of basic features that make connecting, sharing, and engaging with others a seamless and enjoyable experience. Users can create and personalize their profiles, connect with friends, family, and likeminded individuals, and effortlessly share their life moments through text, photos, and videos. "Makemates" prioritizes user privacy and data security, providing a safe environment for users to express themselves freely. With features like status updates, messaging, and a news feed, the application enables real-time communication and interaction with an extensive user base. "Makemates" not only fosters connections but also encourages the discovery of new ideas, trends, and experiences. Join the Makemates community and unlock a world of possibilities for social networking, all within one intuitive and user-friendly application.

CampusTrace- Application for Lost and Found Items on Campus

CampusTrace transforms the systems of lost and found items in campus environments by improving the process of reporting lost or found items and ensures the proper returns of the item to its rightful owners. It enhances social responsibility by encouraging people to collaborate with their owners in recovering the lost. Due to its user-friendly design, Campus Trace simplifies the process of searching for lost items and increases recovery efficiency by giving users a good user experience. The system offers several privacy methods in securing user data and requires admin authentication for item claims. This platform makes it not only easier to recover your lost things but also add cooperation and engagement within the campus community.