Personal Information Security Research Articles

Shopee E-Commerce Website Quality Analysis Based on User Perception Using the Webqual Method

This study aims to analyze the quality of Shopee's e-commerce site using the WebQual method approach. Three main dimensions, namely usability, information quality, and service interaction, are used to evaluate the quality of the Shopee website from the user's point of view. This method measures users' perception of the main aspects that determine their experience in using the Shopee website. The results of the study show that although Shopee has succeeded in providing a responsive and informative platform, several areas need improvement, especially related to aspects of service interaction and the security of users' personal information. In conclusion, Shopee needs to optimize several important aspects to improve the user experience and strengthen its position as a leading e-commerce platform in Southeast Asia. Improved design and navigation of a more modern and intuitive interface can make it easier for users to navigate the platform. In addition, real-time updates of product information regarding stock and prices will help prevent user dissatisfaction. Shopee also needs to improve customer service, both with the addition of staff and technology such as advanced chatbots, to answer customer needs faster. Finally, increased security and transparency regarding transactions and protection of personal data will strengthen user trust in this platform. Through these efforts, Shopee is expected to be able to provide a better shopping experience and maintain the loyalty of its users in the midst of fierce e-commerce competition.

How Personalization Is Shaping Consumer Behavior : An Analysis of Data-Driven Marketing Trends

The paper “How Personalization Is Shaping Consumer Behaviour: An Analysis of Data-Driven Marketing Trends” seeks to also analyse data mining in marketing and the extent to which personalization contributes to consumer behaviour. This relates to how with data and technology the use of Artificial Intelligence can replace and transform marketing and the ability for businesses to offer personalized experience. This means that through email marketers, consumer intimacy will be possible in some ways, which include but not limited to the following: product communication or advertisement. At the same time, the article raises questions, such as security of personal information and the benefits, which may be hazardous, resulting from constant request. The use of AI and machine Learning in personalization in marketing will grow stronger to improve the customers experience and meanwhile ensure a balanced and ethical approach.

Real-Time Prediction of Resident ADL Using Edge-Based Time-Series Ambient Sound Recognition.

To create an effective Ambient Assisted Living (AAL) system that supports the daily activities of patients or the elderly, it is crucial to accurately detect and differentiate user actions to determine the necessary assistance. Traditional intrusive methods, such as wearable or object-attached devices, can interfere with the natural behavior of patients and may lead to resistance. Furthermore, non-intrusive systems that rely on video or sound data processed by servers or the cloud can generate excessive data traffic and raise concerns about the security of personal information. In this study, we developed an edge-based real-time system for detecting Activities of Daily Living (ADL) using ambient noise. Additionally, we introduced an online post-processing method to enhance classification performance and extract activity events from noisy sound in resource-constrained environments. The system, tested with data collected in a living space, achieved high accuracy in classifying ADL-related behaviors in continuous events and successfully generated user activity logs from time-series sound data, enabling further analyses such as ADL assessments. Future work will focus on enhancing detection accuracy and expanding the range of detectable behaviors by integrating the activity logs generated in this study with additional data sources beyond sound.

БЕЗОПАСНОСТЬ ИНФОРМАЦИОННОЙ СФЕРЫ В УСЛОВИЯХ ГЛОБАЛИЗАЦИИ

The article examines the problematic issues of ensuring personal information security, which are an important aspect in the modern world, where information plays a key role. The article specifies the components of personal information security, in particular, the protection of confidential data, personal information and information related to a person from unauthorized access, use, disclosure, violation, modification, verification or destruction.

Designing A Blockchain-Empowered Telehealth Artifact for Decentralized Identity Management and Trustworthy Communication: Interdisciplinary Approach.

Telehealth played a critical role during the COVID-19 pandemic and continues to function as an essential component of health care. Existing platforms cannot ensure privacy and prevent cyberattacks. The main objectives of this study are to understand existing cybersecurity issues in identity management and trustworthy communication processes in telehealth platforms and to design a software architecture integrated with blockchain to improve security and trustworthiness with acceptable performance. We improved personal information security in existing telehealth platforms by adopting an innovative interdisciplinary approach combining design science, social science, and computer science in the health care domain, with prototype implementation. We used the design science research methodology to implement our overall design. We innovated over existing telehealth platforms with blockchain integration that improves health care delivery services in terms of security, privacy, and efficiency. We adopted a user-centric design approach and started with user requirement collection, followed by system functionality development. Overall system implementation facilitates user requirements, thus promoting user behavior for the adoption of the telehealth platform with decentralized identity management and an access control mechanism. Our investigation identified key challenges to identity management and trustworthy communication processes in telehealth platforms used in the current health care domain. By adopting distributed ledger technology, we proposed a decentralized telehealth platform to support identity management and a trustworthy communication process. Our design and prototype implementation using a smart contract-driven telehealth platform to provide decentralized identity management and trustworthy communication with token-based access control addressed several security challenges. This was substantiated by testing with 10,000 simulated transactions across 5 peers in the Rahasak blockchain network. The proposed design provides resistance to common attacks while maintaining a linear time overhead, demonstrating improved security and efficiency in telehealth services. We evaluated the performance in terms of transaction throughput, smart contract execution time, and block generation time. To create a block with 10,000 transactions, it takes 8 seconds on average, which is an acceptable overhead for blockchain-based applications. We identified technical limitations in current telehealth platforms. We presented several design innovations using blockchain to prototype a system. We also presented the implementation details of a unique distributed architecture for a trustworthy communication system. We illustrated how this design can overcome privacy, security, and scalability limitations. Moreover, we illustrated how improving these factors sets the stage for improving and standardizing the application and for the wide adoption of blockchain-enabled telehealth platforms.

A Mobile Application for Anticoagulation Management in Patients After Heart Valve Replacement: A Usability Study.

Individualized anticoagulation therapy is a major challenge for patients after heart valve replacement. Mobile applications assisted by Artificial intelligence (AI) have great potential to meet the individual needs of patients. The study aimed to develop an AI technology-assisted mobile application (app) for anticoagulation management, understand patients' acceptance of such applications, and determine its feasibility. After using the mobile application for anticoagulation management for 2weeks, patients, doctors, and nurses rated its usability using the System Usability Scale (SUS). Additionally, semi-structured interviews were conducted with some patients, doctors, and nurses to gain insights about their thoughts and suggestions regarding the procedure. The study comprised 80 participants, including 38 patients, 18 doctors, and 24 nurses. The average SUS score for patients was 82.37±5.45; for doctors, it was 84.17±5.82; and for nurses, it was 81.88±6.44. This means the patients, physicians, and nurses rated the app highly usable. Semi-structured interviews were conducted on the app's usability with 18 participants (six nurses, three physicians, and nine patients). The interview results revealed that patients found the application of anticoagulation management simple and convenient, with high expectations for a precise dosage recommendation of anticoagulant drugs. Some patients expressed concerns regarding personal information security. Both doctors and nurses believed that elderly patients needed assistance from young family members to use the app and that it could improve patients' anticoagulant self-management ability. Some nurses also mentioned that the use of the app brought great convenience for transitional care. This study confirmed the feasibility of using an AI technology-assisted mobile application for anticoagulation management in patients after heart valve replacement. To further develop this application, challenges lie in continuously improving the accuracy of recommended drug doses, obtaining family support, and ensuring information security.

Secure cognitive health monitoring using a directed acyclic graph-based and AI-enhanced IoMT framework

Millions of people throughout the world struggle with mental health disorders, but the widespread stigma associated with these issues often prevents them from seeking treatment. We propose a novel strategy that integrates Internet of Medical Things (IoMT), DAG-based hedera technology, and Artificial Intelligence (AI) to overcome these challenges. We also consider the costs of chronic diseases such as Parkinson's and Alzheimer's, which often require 24-hour care. Using smart monitoring tools coupled with AI algorithms that can detect early indicators of deterioration, our system aims to provide low-cost, continuous support. Since IoMT data is large in volume, we need a blockchain network with high transaction throughput without compromising the privacy of patient data. To address this concern, we propose to use Hedera technology to ensure the privacy, and security of personal mental health information, scalability and a faster transaction confirmation rate. Overall, this research paper outlines a holistic approach to mental health monitoring that respects privacy, promotes accessibility, and harnesses the potential of emerging technologies. By combining IoMT, Hedera, and AI, we offer a solution that can help break down the barriers that prevent individuals from seeking the support they need for their mental well-being. Furthermore, comparative analysis shows that our best-performing ML models achieve an accuracy of around 98%, which is more than 30% better than traditional models such as logistic regression.

Personal data protection: Between human rights protection and national security

This study aimed to ascertain the equilibrium between safeguarding citizensʼ personal data and maintaining national security in a digital world. The research analysed the regulatory frameworks and judicial practices of the European Union (EU), Ukraine, and the USA through several methodologies. EU regulation offers the most stringent personal data protection, with substantial penalties for infractions. Ukrainian legislation is progressively aligning with European standards; however, procedures for protection and liability require enhancement. The research indicated an increasing tendency in the utilization of artificial intelligence and big data technologies within national security, presenting new issues for safeguarding personal information from disclosure. The research investigated the ethical implications of utilizing such technologies and their potential effects on citizen privacy. The study examined global regulatory procedures, focusing on the European Court of Human Rightsʼ approach to balancing the objectives of safeguarding personal information and national security. The research identified the necessity to broaden the definition of personal data to include communal dimensions and indirect ramifications of data processing in the context of big data and the Internet of Things. This studyʼs findings underscore the importance of an interdisciplinary approach to personal data security, encompassing legal, technological, ethical, and social dimensions. The analysis presented a conceptual model for harmonizing the regulatory framework for the protection of privileged information, including contemporary technical problems and national security requirements. The research holds practical importance for enhancing regulations regarding personal data protection and can assist the formulation of information security plans

Research on Personal Information Security and Privacy Issues in the Network Environment

In today s digital age, we face the grave challenges of personal information security. Frequent hacker attacks, data breaches, social engineering, and illegal information trading pose serious threats to user privacy. By comprehensively applying encryption technology, access control, data desensitization, combined with improved laws and regulations and enhanced user security awareness, these challenges can be effectively addressed, which means, personal privacy can be protected, and a secure and trustworthy network environment can be built.

Personal Information Protection in the Application of Facial Recognition Technology in Public Places - Taking the "First Facial Recognition Case" as an Example

In recent years, the widespread use of facial recognition technology in public places has raised challenges for personal information protection. By analyzing the "First Facial Recognition Case," this article reveals practical challenges under the current legal framework. Significant risks exist in the collection, use, and storage of information, particularly regarding the principles of legality, necessity, and informed consent. The article explores the ambiguity of these principles in legal applications and proposes improvements, including better evaluation of legality and necessity, introducing a tiered consent mechanism, and constructing a dynamic consent mechanism to enhance compliance and protect personal information security.

Privacy-Preserving Prediction of Postoperative Mortality in Multi-Institutional Data: Development and Usability Study.

To circumvent regulatory barriers that limit medical data exchange due to personal information security concerns, we use homomorphic encryption (HE) technology, enabling computation on encrypted data and enhancing privacy. This study explores whether using HE to integrate encrypted multi-institutional data enhances predictive power in research, focusing on the integration feasibility across institutions and determining the optimal size of hospital data sets for improved prediction models. We used data from 341,007 individuals aged 18 years and older who underwent noncardiac surgeries across 3 medical institutions. The study focused on predicting in-hospital mortality within 30 days postoperatively, using secure logistic regression based on HE as the prediction model. We compared the predictive performance of this model using plaintext data from a single institution against a model using encrypted data from multiple institutions. The predictive model using encrypted data from all 3 institutions exhibited the best performance based on area under the receiver operating characteristic curve (0.941); the model combining Asan Medical Center (AMC) and Seoul National University Hospital (SNUH) data exhibited the best predictive performance based on area under the precision-recall curve (0.132). Both Ewha Womans University Medical Center and SNUH demonstrated improvement in predictive power for their own institutions upon their respective data's addition to the AMC data. Prediction models using multi-institutional data sets processed with HE outperformed those using single-institution data sets, especially when our model adaptation approach was applied, which was further validated on a smaller host hospital with a limited data set.

Applications of Medical Big Data and Personal Information Protection

In the current social environment, the generation and application of medical big data is closely related to the personal information security of every citizen. With the continuous development of medical technology, the application of medical big data has become increasingly widespread. On one hand, it can promote the development of medical technology, including drug development, personalized medicine, and precision treatment. On the other hand, medical big data also concerns the private information of citizens, which is a crucial part of personal information protection. However, in practice, there often exists a contradiction between medical big data and the protection of citizens' personal information rights. From the perspective of medical big data, there are overlaps and differences among citizens' personal information, privacy information, medical health information, and medical big data information. Relying solely on the “informed consent” rule for personal information cannot effectively protect personal information comprehensively or guarantee the application and development of medical big data. This article analyzes the issues surrounding Chinese medical big data in terms of personal information from a legal perspective, and proposes corresponding solutions to these issues. It is hoped that this research can contribute to advancing the legalization of medical data openness, re-examining patients' personal information in the context of medical big data, and balancing the conflicts between individual privacy interests, public interests, and economic benefits of medical data, thus better protecting individuals' personal information interests under social and private benefits.

Enhanced fluorescence imaging of level 1–3 latent fingerprints on multi-materials through lipophilicity-tunable probes

Latent fingerprints (LFPs) are crucial for personal information security and forensic medicine. In recent years, there has been a lot of attention on fluorescence probes for LFP imaging, but the effect of lipophilicity on LFP's imaging performance has not been fully explored. Herein, three naphthalimide-based AIE probes (TPA-NA-Pr, TPA-NA-OH, TPA-COOH) with different lipophilicity were designed and analyzed. After optimizations, TPA-NA-Pr and TPA-NA-OH were found to be able to visualize LFPs within 2 seconds under 405 nm LED lamp excitation. By immersing materials with the probe solution or spraying the probe solution on different material surfaces, a series of high-quality LFP images could be quickly developed. Even LFP level 3 details (such as sweat pores) could be directly observed with naked eyes. The probe solution stored for 2 months did not precipitate or deteriorate, but still can be used for accurate LFP imaging on different substrates with high-resolution level 1–3 details. This study provides insights into the role of lipophilicity on LFP probes and furnishes two LFP probes with extended lifespan and reduced costs for their real-life applications in personal identification and criminal investigation scenarios.

How to mitigate the risks of deployment of artificial intelligence in medicine?

The aim of this study is to examine the risks associated with the use of artificial intelligence (AI) in medicine and to offer policy suggestions to reduce these risks and optimize the benefits of AI technology. AI is a multifaceted technology. If harnessed effectively, it has the capacity to significantly impact the future of humanity in the field of health, as well as in several other areas. However, the rapid spread of this technology also raises significant ethical, legal, and social issues. This study examines the potential dangers of AI integration in medicine by reviewing current scientific work and exploring strategies to mitigate these risks. Biases in data sets for AI systems can lead to inequities in health care. Educational data that is narrowly represented based on a demographic group can lead to biased results from AI systems for those who do not belong to that group. In addition, the concepts of explainability and accountability in AI systems could create challenges for healthcare professionals in understanding and evaluating AI-generated diagnoses or treatment recommendations. This could jeopardize patient safety and lead to the selection of inappropriate treatments. Ensuring the security of personal health information will be critical as AI systems become more widespread. Therefore, improving patient privacy and security protocols for AI systems is imperative. The report offers suggestions for reducing the risks associated with the increasing use of AI systems in the medical sector. These include increasing AI literacy, implementing a participatory society-in-the-loop management strategy, and creating ongoing education and auditing systems. Integrating ethical principles and cultural values into the design of AI systems can help reduce healthcare disparities and improve patient care. Implementing these recommendations will ensure the efficient and equitable use of AI systems in medicine, improve the quality of healthcare services, and ensure patient safety.

Legal Mechanisms for the Protection of Personal Information in Public Surveillance

How public surveillance balances the relationship between preventing and combating crime and protecting citizens' personal information is a social issue of common concern to the people. With the advent of the digital age and the unprecedented development of Internet technology, public surveillance has spread to all corners of people's lives. Although big data technology brings a lot of convenience to people's daily work and life, it is also prone to cause personal information security problems. For example, the leakage of personal information and the release of personal information have had a great impact on the personal property and mental aspects of citizens. Lack of protection of citizens' right to personal information in public areas, exposing citizens to surveillance as if they were transparent. A strong sense of being monitored and controlled seriously affects the happiness index of citizens. Therefore, the question of how to use public surveillance to safeguard the public interest without infringing on individual privacy is particularly important.

The Shield of Privacy in the Digital Age: The Clash between Facial Recognition Technology and Personal Information ProtectionCase Analysis and Strategy Discussion

Driven by the wave of digitalization, facial recognition technology has become a key tool for identity verification and security monitoring. However, with its widespread application in daily life, issues of personal privacy security have also become prominent. This study delves into the complexities of facial recognition technology in the realm of personal information security through case analysis, revealing its convenience as an identity verification tool and the risks it poses as a potential means of privacy infringement. The research aims to identify and analyze the ethical and legal issues faced by facial recognition technology and to propose strategies for strengthening personal information protection. This paper integrates literature review, case studies, and international comparative analysis to provide support and recommendations for the practice and policy-making of personal information protection in the digital age.

Conceptual Framework for Unified E-Government Web Platform

Abstract Technological development has an impact on various areas, and governments from all over the world search to make a smooth transition from traditional to digital, by adopting dedicated software solutions aiming to facilitate the interaction of citizens with public institutions. These platforms are designed to improve the citizens relationship with government by facilitating various processes and tasks. The importance of having a unified web platform is easily justified. Citizens are no longer in need of searching different platforms for their specific needs, dealing with a lot of interfaces and accounts, as all are gathered in one, maintaining a robust, complex system that is easy to use. Therefore, they would benefit from an integrated digital environment, that meet their needs and expectations, also providing security and privacy of confidential and personal information. The authors present the value of creating such an environment, that has a high technical level, that is user-centered and accessible for interactions between citizens and government. Designing and implementing a unified e-government web platform requires the analysis of various factors and aspects such as user interface, processes, services, security measures, integration with social media platforms, collaborative environment, and many others. First, an investigation on specific literature was made to offer a context to the present research and be used as a starting point, highlighting the main factors related to e-government and the impact of this type of platforms. The model proposed by the authors encapsulates considerations on both sides: government and citizens to offer a detailed understanding on e-government platforms.

Intrinsically Stretchable Floating Gate Memory Transistors for Data Storage of Electronic Skin Devices.

To propel electronic skin (e-skin) to the next level by integrating artificial intelligence features with advanced sensory capabilities, it is imperative to develop stretchable memory device technology. A stretchable memory device for e-skin must offer, in particular, long-term data storage while ensuring the security of personal information under any type of deformation. However, despite the significance of these needs, technology related to stretchable memory devices remains in its infancy. Here, we report an intrinsically stretchable floating gate (FG) polymer memory transistor. The device features a dual-stimuli (optical and electrical) writing system to prevent easy erasure of recorded data. An FG comprising an intermixture of Ag nanoparticles and elastomer and with proper energy-band alignment between the semiconductor and dielectric facilitated sustainable memory performance, while achieving a high memory on/off ratio (>105) and a long retention time (106 s) with the ability to withstand 50% uniaxial or 30% biaxial strain. In addition, our memory transistor exhibited high mechanical durability over multiple stretching cycles (1000 times), along with excellent environmental stability with respect to factors such as temperature, moisture, air, and delamination. Finally, we fabricated a 7 × 7 active-matrix memory transistor array for personalized storage of e-skin data and successfully demonstrated its functionality.

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration.

Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

Criminal Protection of Public Personal Information in the Digital Era

The digital era has accelerated the speed and breadth of information dissemination. While enjoying the information dividend, the security of personal information has hidden dangers of leakage. For public personal information, this concern is even worse. On the theoretical level, the secondary authorization theory, the purposiveness theory, and the objective openness standard theory cannot fully respond to the reality and urgency of repeated violations of public personal information. On the level of judicial practices, the guiding cases of personal information protection issued by the Supreme People’s Court enlighten us that we should give classified protection to public personal information. Typed protection mode classifies the public personal information from the perspectives of voluntary disclosure, compulsory disclosure, and illegal disclosure, and considers whether it is convicted or not from three aspects: the subjective illegality of the doer, the scope of information control of the information subject and the measurement of public and private interests, which strengthens the protection of the public personal information.