Protect User Data Research Articles

Enhancing smartphone security with human centric bimodal fallback authentication leveraging sensors.

Smartphones store valuable personal information, necessitating robust authentication methods to protect user data. This research proposes a lightweight bi-model fallback authentication technique that combines dynamic security questions and finger pattern recognition using inertial measurement units. The dynamic security questions are generated based on the smartphone's usage behavior, while the owner's finger movements are captured using four different inertial sensors: accelerometer, gyroscope, gravity sensor, and magnetometer. By combining the answers to the questions and the owner's finger movements, the user can be authenticated even if the primary authentication method fails. In this study, data was collected from 24 participants, including 12 primary phone users and 12 close adversaries, over a span of 28 days. The dynamic security questions, derived from call, SMS, battery charging events, application usage, location, and physical activity categories, achieved high accuracy rates, with call, SMS, and application usage surpassing . Incorporating the inertial measurement units significantly improved the accuracy of all question types, increasing from a maximum of to , while also enhancing the True Positive Rate from 0.79 to 0.99 compared to a previous study. This research presents a promising lightweight bi-model fallback authentication technique that leverages dynamic security questions and inertial measurement units data, demonstrating its effectiveness for enhancing smartphone security.

A Survey on User Profiling, Data Collection, and Privacy Issues of Internet Services

Users are usually required to share several types of data, including their personal data, as different providers strive to offer high-quality services that are often tailored to end-users’ preferences. However, when it comes to personalizing services, there are several challenges for meeting user’s needs and preferences. For content personalization and delivery of services to end users, services typically create user profiles. When user profiles are created, user data is collected and organized to meet the personalization requirements of the services. In this paper, we provide an overview of current research activities that focus on user profiling and ways to protect user data privacy. The paper presents different types of data that services collect from users on examples of commonly used Internet services. It proposes data categorization as a prerequisite for controlled data sharing between users and Internet services. Furthermore, it discusses how data generalization can be used for anonymization purposes on examples of the proposed data categories. Finally, it gives an overview of the privacy framework being developed and gives guidelines for future work focusing on data generalization methods in order to reduce user privacy risks.

Assessing Privacy Policies and App Settings for User Data Protection: A Data Subject‐Centered Framework Analysis of TikTok in the U.S. and Europe (2023–2024)

ABSTRACTThis study examines the extent to which TikTok's privacy policies and app settings in the U.S. and Europe protect the rights entailed in the data subject‐centered framework. Using a case study approach, we analyze current policy documents and app settings to identify the alignment of TikTok's policies with the GDPR perspective. Our findings reveal that current policies and settings fall short in key areas. First, TikTok policies lack details related to managing and protecting sensitive data. Second, the policies neglect to discuss the responsibilities of social media companies when such data is utilized by unspecified third parties. Furthermore, there is a noticeable deficiency in the U.S. regarding detailed in‐app privacy notices and setting options, especially in terms of managing location data and advertisements. Additionally, there is a need for explanations on how specific settings impact users. Lastly, a critical demand exists for default settings, including those for advertisements, to enhance data protection.

A digital service standardization model for Nigeria: The role of NITDA in regulatory compliance

The digital transformation in Nigeria has accelerated the need for effective standards and regulations to ensure quality and interoperability of digital services. The National Information Technology Development Agency (NITDA) plays a crucial role in this context by overseeing regulatory compliance and fostering a conducive environment for digital innovation. This review presents a comprehensive model for digital service standardization tailored to Nigeria's unique socio-economic and technological landscape. The proposed model emphasizes the importance of regulatory frameworks established by NITDA in promoting consistency, reliability, and security across various digital platforms and services. The model outlines key components necessary for effective standardization, including policy formulation, compliance monitoring, and stakeholder engagement. It integrates best practices from international standards while adapting them to local needs and challenges. Central to the model is the role of NITDA in developing and enforcing standards that align with global benchmarks, thereby ensuring that Nigerian digital services are competitive and secure. A significant aspect of the model is its focus on regulatory compliance, which is vital for maintaining the integrity of digital services and protecting user data. The review discusses how NITDA's regulatory initiatives address common issues such as data privacy, cybersecurity, and service quality. By examining case studies and industry feedback, the review highlights the impact of NITDA's efforts on improving the overall digital service ecosystem in Nigeria. The model also addresses the need for continuous evaluation and adaptation of standards in response to technological advancements and emerging threats. It proposes mechanisms for regular updates and revisions to standards, ensuring that they remain relevant and effective. Additionally, the review explores the role of collaborative partnerships between NITDA, industry stakeholders, and academic institutions in advancing digital service standardization. The proposed digital service standardization model provides a structured approach to enhancing regulatory compliance and fostering a robust digital environment in Nigeria. It underscores the pivotal role of NITDA in driving these efforts and offers a framework for ongoing improvement and adaptation to the dynamic digital landscape.

5G Communication Network and Federated Learning for College Accounting Informatization Teaching

At present, the social economy is entering the information age represented by computer, communication technology and network technology as the core, and the continuous development of modern information technology will certainly have a great impact on the teaching mode, content and methods of traditional accounting computerization. We aim to improve the existing higher vocational accounting teaching mode by building a multi-integrated teaching mode through federated learning based on 5G communication network as an environment for efficient information transfer. In addition, we develop a joint optimization strategy for priority-dependent task offloading, wireless bandwidth, and computational power in a distributed machine learning approach to ensure that more resources are allocated to users with higher priority while protecting user data privacy and reducing learning overhead. We have conducted extensive simulation experiments for both environments, and these simulation results demonstrate the effectiveness of our proposed solutions for different problems from different perspectives.

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.

Enhancing Privacy on Online Social Networs: A comparative study of encryption techniques

The ability to connect with people all over the world and share private information has made online social networks (OSNs) indispensable to contemporary communication. Because of the serious privacy and security concerns that come with their fast growth, protecting user data is of the utmost importance. In order to improve OSN privacy, this study examines several encryption techniques. It discusses symmetric encryption (ECC and RSA) and synthetic encryption (AES) as well as hybrid methods that combine the two types of encryption. Using examples from well-known sites such as LinkedIn, Facebook, and Twitter, the research assesses the efficacy and practicality of various strategies. Key management, performance implications, and user experience maintenance are some of the challenges that are addressed while adopting comprehensive encryption. To further improve privacy protection in OSNs, the paper delves into upcoming developments in encryption technology, such as decentralized encryption approaches and algorithms that are resistant to quantum computing. In order to better understand how to improve OSN security, this article will provide a thorough examination of encryption's function in protecting user privacy.

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.

Unraveling the Potential of Decentralized Finance: A Comprehensive Analysis of Opportunities, Risks, and Future Trends

The financial sector is going through a major transformation and evolution with the emergence of Decentralized Finance (DeFi). It is a modern concept that is reshaping the traditional financial landscape and thus changing traditional financial structures. This research delves into the term “Decentralized Finance” by exploring its various advantages, associated challenges, and future patterns and trends. Leveraging blockchain technology, DeFi offers new financial services without intermediaries that in turn enhance financial inclusion, autonomy, and economic empowerment of individuals, though this also entails enormous risks and challenges, including vulnerability to smart contract vulnerabilities, regulatory uncertainty, and market volatility. This research also attempts, through a systematic literature review, to identify the most significant opportunities for DeFi, such as democratized access to financial services, lower transaction fees, and increased liquidity. Additionally, this research highlights the key challenges facing the development of DeFi, such as limitations on its ability to handle large transactions, concerns about user data protection, and the need to adhere to regulatory standards. The paper concludes with a view and insights into the future of DeFi, emphasizing the importance of regulatory clarity, technological innovation, and community governance in shaping the trajectory of DeFi. This analysis contributes to gaining a deeper understanding of the potential and capabilities of DeFi, and provides stakeholders with indispensable insights to navigate the dynamics of DeFi.

An efficient anonymous authentication scheme for blockchain assisted and fog-enabled smart grid

The growing integration of blockchain and fog computing with Smart Grid (SG) technologies highlights the need for strong security protocols, dependable communication channels, and sensitive user data protection. In order to meet the unique needs of SG, this research paper presents a novel and effective anonymous authentication scheme enhanced by fog computing and blockchain. The suggested scheme makes use of blockchain's decentralized structure to strengthen security protocols and ensure user privacy and anonymity within the SG ecosystem. Through the combination of fog computing's low latency benefits and blockchain's immutability, the suggested scheme creates a robust framework for secure authentication. The suggested approach gives considerable weight to reducing computational overhead and optimizing user identity privacy. Through comprehensive analysis, the study highlights the scheme's efficiency concerning authentication speed, resource utilization, and resilience against potential attacks. These results offer an important remedy for the ever-changing landscape of energy management systems and substantially advance the development of safe and privacy-preserving SG architectures.

Enhancing privacy policy comprehension through Privacify: A user-centric approach using advanced language models

As the digital age advances, the collection, usage, and dissemination of personal data have become critical concerns for users, regulators, and the cybersecurity community. Questions surrounding the extent of identifiable data collection, its usage, sharing, selling, and the mechanisms of consent are increasingly central to discussions on user data privacy. These issues highlight the need for effective management and comprehension of privacy policies. To this end, this paper introduces Privacify— a production-ready web application designed to enhance the accessibility and understandability of privacy policies, thus empowering users to make more informed decisions about their data. At its backend, Privacify leverages a combination of text segmentation, summarization using Large Language Model (LLM), and map-reduce technologies to facilitate BASE analysis for single-document insights and WRT and REV for comprehensive cross-document analysis. Designed with a user-centric approach, Privacify features an intuitive interface that presents all relevant user privacy information in easy-to-understand language, complete with a detailed explainability component. This design not only simplifies privacy policies but also aids users in effortlessly navigating complex privacy terms, significantly boosting their ability to protect and manage their personal information. Our evaluation employs robust methodologies, including reliability and accuracy assessments, alongside rigorous functionality verification through ROUGE metrics and human analysis, validating the system’s efficacy and performance. Privacify’s architecture promotes scalability, replicability, and seamless deployment, advancing the domain of user data protection through improved privacy comprehension.

Guiding the way: a comprehensive examination of AI guidelines in global media

AbstractWith the increasing adoption of artificial intelligence (AI) technologies in the news industry, media organizations have begun publishing guidelines that aim to promote the responsible, ethical, and unbiased implementation of AI-based technologies. These guidelines are expected to serve journalists and media workers by establishing best practices and a framework that helps them navigate ever-evolving AI tools. Drawing on institutional theory and digital inequality concepts, this study analyzes 37 AI guidelines for media purposes in 17 countries. Our analysis reveals key thematic areas, such as transparency, accountability, fairness, privacy, and the preservation of journalistic values. Results highlight shared principles and best practices that emerge from these guidelines, including the importance of human oversight, explainability of AI systems, disclosure of automated content, and protection of user data. However, the geographical distribution of these guidelines, highlighting the dominance of Western nations, particularly North America and Europe, can further ongoing concerns about power asymmetries in AI adoption and consequently isomorphism outside these regions. Our results may serve as a resource for news organizations, policymakers, and stakeholders looking to navigate the complex AI development toward creating a more inclusive and equitable digital future for the media industry worldwide.

Realization of Reliable and Effective Authentication in Intelligent Systems by Using Visual Biometrics Methods

The main purpose of this article is to consider the aspects of ensuring security and increasing the efficiency of the authentication process in intelligent systems using visual biometrics. The work is aimed at the development and improvement of authentication systems using advanced biometric identification methods. An intelligent system has been created that ensures secure authentication of users of the current system, using a Siamese neural network. In addition to the implementation of basic security measures in the form of hashing and saving user logins and passwords, the implementation of two-factor authentication is important nowadays, which significantly strengthens the protection of user data and prevents most modern methods of hacking and stealing user data. Two-factor authentication is implemented as a technology for searching, recognizing and comparing the faces of system users, as visual biometrics is more secure than other types of two-factor authentication. Different variations of the possible implementation of Siamese neural network using Contrastive loss function and more modern Triplet loss function were reviewed and accordingly, a neural network using Triplet loss function was accomplished and trained. After training and verifying the correct operation of the neural network, it was integrated into the created intelligent system, thanks to which an effective way of recognizing the face of the system user was created, saving the received information in the database and further comparing the current user with the stored face during authentication. As a result, a secure and reliable intelligent system was created that cutting down the risk of unapproved access to the user account and uses an effective and modern method of user authentication.

A privacy-preserving federated graph learning framework for threat detection in IoT trigger-action programming

Trigger-Action Programming (TAP) is a common user-programming paradigm in Internet of Things (IoT) smart home platforms, allowing users to create customized automation rules to match IoT devices and network services. However, the potential security threats associated with TAP rules are often overlooked or underestimated by users. To address this issue, we propose PFTAP, a novel federated graph learning framework for threat detection of TAP rules while simultaneously protecting user data and privacy. First, we propose a hierarchical graph attention network. This network comprises intra-rule attention and inter-rule attention modules, which enable the learning of comprehensive feature representations for triggers and actions. By capturing the intricate relationships between different rules, the network enhances the detection accuracy of risky TAP rules. Moreover, our framework is based on federated learning and integrates symmetric encryption and local differential privacy techniques, aiming to safeguard user privacy from unauthorized access or tampering. To evaluate the effectiveness of our framework, we conduct experiments using an extensive dataset of IFTTT rules. The experimental results convincingly demonstrate that PFTAP outperforms state-of-the-art methods in terms of threat detection performance.

Federated Learning for Weld Quality Prediction

This study introduces a monitoring system that can accurately predict the quality of friction stir welds. The study involved four different sets of welding experiments using varying materials and tool configurations. The goal was to create a universal monitoring system capable of predicting weld quality across these diverse experimental sets. Real-time welding data was collected using instruments such as load cells and a power sensor. Signal processing methods were then used to analyze this data and extract essential information about welding quality. Subsequently, Federated Learning (FL) was used to develop the universal monitoring system. This method involves collective learning and model training, leading to a global model trained on decentralized data from the different experimental sets. The approach proved to be effective in forecasting weld quality, achieving a mean absolute error (MAE) of 10.44 for all welding experiments. Additionally, it offered benefits such as reduced latency and enhanced user data protection, while maintaining the accuracy of the global model. An artificial neural network (ANN) model was also developed for comparison with FL, achieving a MAE of 13.85 for the welding experiments. Overall, this study demonstrates the effectiveness of training global and more reliable models, with multiple devices sharing their knowledgebases to train the global model effectively. Conflict of Interest Statement The authors declare no conflicts of interest.

Lumina Eventix: OPTIMIZING EVENT MANAGEMENT THROUGH DIGITAL PLATFORMS

The proposed project is a comprehensive web-based Event Management System (EMS) designed to streamline the organization and management of various events, including conferences, workshops, concerts, weddings, and other ceremonies. This platform aims to provide a user-friendly interface for both event organizers and customers, facilitating the entire event planning process from inception to execution. Customers will be able to specify their event requirements, such as sound systems, food management, and other services. The system will then provide an estimated budget and suggest cost-effective ways to allocate their resources. Additionally, EMS will recommend the best event organizers based on the customer's budget and specific needs, enhancing the planning process with tailored suggestions. The platform will integrate secure payment gateways to handle transactions efficiently, ensuring a smooth process for both organizers and customers. The platform will offer robust registration management capabilities, allowing organizers to track attendee registrations, manage waitlists, and generate comprehensive reports. Embedded communication tools will enable organizers to send updates and reminders to attendees via email and SMS, ensuring seamless communication throughout the event lifecycle. Security and data privacy will be prioritized, with measures in place to protect user data and ensure compliance with relevant regulations. The platform will be built with scalability in mind, capable of handling events of varying sizes and complexities. In summary, this Event Management System aims to simplify the complexities associated with event organization, providing a one-stop solution that enhances efficiency, improves attendee engagement, and ultimately contributes to the success of any event. Key Words: Event Management System (EMS), Event planning, User-friendly interface, Registration management, Payment gateways, Communication tools, Scalability

PENERAPAN ADVANCED ENCRYPTION STANDARD UNTUK SISTEM LOGIN DAN REGISTRASI PADA SISTEM INFORMASI PENJUALAN (STUDI KASUS:TOKO ILHAM BANJAR)

Data security in sales information systems has become a critical priority in today's digital era. Ilham Banjar Store needs a system that can effectively protect user data, especially in login and registration features, which are vulnerable to security threats. The lack of encryption mechanisms in the current system results in user data, such as passwords, being transmitted in plaintext, increasing the risk of unauthorized access and data modification. Therefore, this study aims to enhance the security of the Ilham Banjar Store's sales information system by implementing the Advanced Encryption Standard (AES) 256-bit Cipher Block Chaining (CBC) method in the login and registration processes. The research method used is Research and Development (RD), consisting of stages such as literature review, needs analysis, system design, implementation, testing, evaluation, and analysis. The implementation of the Advanced Encryption Standard (AES) involves the encryption processes of AddRoundKey, SubBytes, ShiftRows, and MixColumns, and the decryption processes are the inverse of these encryption steps. Testing includes measuring encryption and decryption times as well as verifying data integrity using HMAC (Hash-Based Message Authentication Code). The research results show that the performance testing indicates encryption and decryption times remain within acceptable limits, and the encrypted and decrypted data maintain their integrity. Thus, the new system meets both security and performance requirements.

Analisis Aplikasi Augmented Reality dalam Bidang Pariwisata Terhadap Keamanan Privasi Data

This study investigates the benefits of augmented reality (AR) in the tourism industry and its impact on data security. The background of the research highlights that information technology is becoming increasingly important in the tourism industry, with augmented reality (AR) being a major focus for enhancing visitor experiences. In this context, concerns about user data protection arise since AR applications may require personal information such as user location and travel preferences. A comparative research method was used to compare two popular augmented reality (AR) applications: Smartify, Arts and Culture, Zome, Augmented Reality, Maps AR, Guideo, and AR Trial. The results indicate that most applications have adequate data security policies, including data transfer encryption and user control over their data. Zome was found to be the best in terms of data security and user privacy, scoring 90% in the assessment. This research underscores the importance of using AR applications to maintain data security and user privacy in the tourism industry and suggests further research to learn more about the data privacy practices of AR applications.

Сучасні методи автентифікації та авторизації користувачів розподілених веборієнтованих систем

The analysis was carried out and the results of a comparative review of the most common methods of authentication and authorization of users of web-oriented systems with a distributed architecture were presented. Considering the relevance of cybersecurity issues in the digital age, the research focuses on identifying effective strategies for protecting user data in the development of distributed web-oriented systems within the trade sector. The most likely threats to data access, characteristic of distributed web-based systems, have been studied, and the potential causes of these vulnerabilities have been determined. Particular attention in the publication is devoted to assessing the risks and benefits of various approaches, including basic authentication, session-based authentication, JWT tokens, and access and refresh tokens (OAuth 2.0 standard). Various aspects of each method have been analyzed, particularly their reliability and vulnerability to attacks. The work discusses real cases of vulnerabilities in distributed web-oriented systems and offer recommendations for their elimination to enhance the security of online trading platforms.

Study and Review of GupShup- A Chat Application

In the digital age, the demand for secure and private messaging platforms has surged, paralleling the exponential growth of online interactions. This research introduces Secure GupShup, a groundbreaking application designed to address the paramount concerns surrounding user privacy and data security. In an environment where information confidentiality is of utmost importance, Secure GupShup stands out by providing a feature-rich experience that includes private and public chat rooms, with a distinctive emphasis on security through the implementation of instant disappearing messages. The core innovation of Secure GupShup lies in its implementation of instant disappearing messages, a feature designed to augment user privacy. By ensuring that messages automatically vanish after a predetermined time, the application adds an extra layer of security, preventing unauthorized access to sensitive information. This not only grants users greater control over their conversations but also fosters an environment where information is transient, mitigating the risks associated with data retention. The research delves into the intricate architecture of Secure GupShup, exploring the amalgamation of cutting-edge encryption protocols and intuitive user interface design. The application's robust security measures, coupled with its user-friendly interface, position Secure GupShup as a formidable contender in the realm of secure communication applications. As the digital landscape continues to evolve, Secure GupShup signifies a pivotal step towards meeting the escalating demands for privacy and security in the realm of digital communication. The incorporation of end-to-end encryption protocols ensures that messages exchanged within Secure GupShup remain private and secure. Users can communicate confidently, knowing that their conversations are shielded from unauthorized access, providing a heightened level of privacy. The inclusion of disappearing messages adds an extra layer of security by automatically removing sensitive content after a designated time period. This feature not only protects user data from long-term exposure but also aligns with the evolving need for ephemeral communication in digital platforms. While acknowledging the current limitations, the proposed solutions aim to address these challenges and enhance the overall functionality of Secure GupShup. The outlined future work envisions continued improvements, embracing emerging technologies and user preferences to maintain Secure GupShup's position as a leading secure communication platform..