Personal Information Security Research Articles

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.

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.

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.

Protection of Personal Data in Telemedicine

The protection of personal data in telemedicine in the context of current legislation and regulatory acts of the European Union is considered. Social relations in the field of use of personal data in telemedicine are studied, in particular, legal regulation of the use of personal data processed in health care information systems. In the information society, the right to health care and medical care is closely related to the right to access information. The right to access to information is a guarantee of the exercise of the right to health care and medical care, defines its content and contributes to equal access of every person to medical care. It is indicated that ensuring the balance of private and public interests in telemedicine requires differentiation of the conditions for processing personal health data. As an additional condition for the processing of personal data for specific purposes, it is proposed to provide for the consent of the subject of personal data to data processing with the right to refuse processing. Such purposes may include, in particular, the processing of electronic health records in the unified state health information system, the processing of depersonalized personal health data for research purposes. This approach contributes to the protection of public interests in health care and takes into account the autonomy of the will of the subject of personal data, his interests in the field of personal information security, corresponds to approaches to legal regulation in the European Union.

Legal Regulation Risks and Insights Related to Cross-Border Flow of Data

The primary objective of this research is to ensure the secure and compliant transmission of personal information from China. To this end, the Chinese government has implemented the "Standard Contract Measures for the Export of Personal Information." This study employs both literature review and factor analysis methods to meticulously examine the potential risks in the practical implementation of these measures, and further suggests strategies to address the current challenges. Additionally, the study introduces foreign systems for reference, aiming to guide Chinese enterprises in circumventing legal risks associated with cross-border data flows and ensuring the security and compliance of personal information departing from the country. The findings of this research reveal that the existing legal framework for cross-border data flows in China has certain shortcomings, providing valuable insights for the improvement of China's legal framework and related regulatory systems. Furthermore, it offers practical cautionary value for Chinese enterprises in their operations.

The intention of continual use of dockless bike sharing connecting with metro: A perspective based on loyal users

The aim of this study is to explore the factors influencing the intention to continually use dockless bike sharing connecting with metro in the context of various new convenient modes of transportation. Firstly, based on the questionnaires collected in Nanjing, China, basic attributes and travel characteristics of users who use dockless bike sharing to connect with the metro are compared and analyzed. Secondly, an ordered logit model is established to investigate the influencing factors of the continuous usage intention of the dockless bike sharing connecting with metro. The model results indicate that: (1) the increase in the monthly income and the education level has a restraining effect on the intention to continue using dockless bike sharing connecting with the metro, although this trend of reduced usage intention is less pronounced among users with higher education levels. (2) Travel duration and travel time also negatively affect the intention to continue using this mode. Interestingly, during peak hours on weekdays, users exhibit a higher willingness to use dockless bike sharing connecting with the metro. (3) Factors such as saving time, solving taxi availability issues, improving accessibility in remote areas, and users' environmental consciousness encourage the continued use of this travel mode. In contrast, high accessibility, network congestion, difficulties in finding parking lots, personal information security risks, high costs, imbalances in supply and demand during peak periods, traffic congestion, and bad weather discourage users to continuously use this mode. Finally, based on these findings, several policies and suggestions to promote the continuous use of dockless bike sharing connecting with the metro are proposed.

Predicting The Customer Purchase Intention Based on Perception of Risk Using Machine Learning Techniques

Online shopping has become an indispensable aspect of modern life. Nevertheless, this convenience is not without its risks. Potential pitfalls include fraudulent activity, subpar product quality, and the security of personal and financial information. The purpose of this research paper is to predict how individuals react in terms of online purchase intention, guided by their unique risk perceptions. It also aims to explore important risks and sociodemographic factors influencing the levels of perceived risk. This investigation was conducted through a survey questionnaire administered to a sample of 308 participants from India. Various machine learning models were applied to predict the customer purchase intention based on the various risks. The results revealed that the CatBoost Classifier outperformed the other methods. Following closely, Random Forest and Gradient Boost Classifier also demonstrated strong performance. By utilizing Random Forest's feature importance, factors such as financial risk, delivery risk, perceived health risk, time loss risk, and cultural risk significantly impact purchase intention. Additionally, among demographic factors, occupation has the most significant influence. This prediction model will offer practical insights for e-commerce platforms, marketers, and policymakers, enabling them to tailor strategies.

Research on the legal framework of personal information privacy protection in automated administrative environment: centered on data security and rights balance

In the digital age, with the increasing importance of personal information, especially in the field of automated administration, the protection of personal information is particularly urgent. Ensuring the security of personal information in automated administration not only helps to safeguard the realization of individual dignity rights and meet the inherent requirements of the principle of proportionality, but also regulates the use of algorithms to avoid discrimination. However, although the legislation on the protection of personal information in automated administration has made some progress, there are still some shortcomings: the legislation is not directly specified, the law is not operable, the supervision and management mechanism is not perfect; There are also problems at the law enforcement level: improper use of immunity in administrative procedures, excessive expansion of the scope of personal information processing; In the relief aspect, there are also challenges: the provisions of administrative public interest litigation are vague, and there are loopholes in the way of liability. In order to improve the protection mechanism of personal information in automated administration, it is necessary to expand the direct regulation of automated administration in legislation, enhance the operability of laws, and improve the supervision and management mechanism of personal information. At the same time, it is also necessary to amend the administrative due process and strictly implement the principle of least necessity at the level of law enforcement. In terms of relief, the applicable rules of administrative public interest litigation should be clarified, and more targeted ways of assigning blame should be established.

“Those blimmin Ts and Cs”: a mixed methods analysis of how people manage personal information, privacy, and impressions

ABSTRACT Interconnected and smart technologies complicate personal information management (PIM) because users delegate the storing, organizing, and retrieving of personal information to smart and mobile service providers. Meta-level PIM activities are required to maintain the privacy and security of personal information. This study provides insights into how users of location tracking, mobile apps, and smart home technologies perceive PIM and privacy. We turn to the privacy as contextual integrity (CI) and impression management (IM) literatures to explore informational norms and interpersonal dynamics in PIM. This study is based on a mixed methods design to analyze focus groups and interviews with 106 British and Dutch respondents. Combining unsupervised Latent Dirichlet allocation (LDA) topic modeling and thematic analysis, we reveal discursive patterns in respondent accounts of technology use and provide an in-depth interpretation of these patterns. Our findings indicate that PIM practices are associated with the perceived appropriateness of information flows, anthropomorphic interpretations of technologies, and interpersonal surveillance. Thus, impressions are managed toward social actors as well as technology providers. We contribute to PIM research with a demonstration of how PIM in mobile, smart, and location-based technology use cannot be separated from contextual factors and strategies to manage impressions of habits and behaviors.

Research on the Status Quo and Dilemma of Minors' Personal Information Protection

With the rapid development of mobile Internet, minors' personal information is inevitably collected and processed by various information processors. Due to the characteristics of immature mental development and weak risk prediction ability of minors, it is difficult to effectively protect their personal information security. Although China's "Personal Information Protection Law" makes provisions on the protection of minors' personal information, there are still some problems such as "one-size-fits-all" age limit and imperfect guardian consent system. This paper will test the current hundreds of various types of apps, conduct a questionnaire survey on the protection of personal information of minors and their guardians, analyze the current dilemma of the protection of personal information of minors at the social level, propose countermeasures according to the symptoms, and explore more targeted ways to protect the rights and interests of minors' personal information. Escort the physical and mental healthy development of minors under the background of the information age.

Формирование принципов научной этики у студентов при изучении иностранного языка

The paper is aimed at analyzing the scientific information security problems when learning a foreign language. It clarifies the definition of personal security, in general, and information security, in particular, in scientific ethics context. The paper considers the communicatively conditioned approaches to mastering the scientific ethics principles by the researchers from the viewpoint of using linguistic means for different citation types and special learning process organization with moral, cognitive and linguistic aspects having been taken to the account. The research emphasizes the information component importance in the process of mastering linguistic disciplines. It describes the scientific ethics development levels with the foreign language means being used in researchers. The paper compares the outcomes of developing the skills in the field of scientific ethics compliance for experimental and controlling students` groups in foreign language classes.

Application and analysis of face matching based on the Siamese model in face recognition

In recent years, face recognition technology, and face matching particularly have broadened the application fields in various aspects of society. It is considered a combination of deep learning architecture and face recognition technology, which has been used for personal information security and safety efficiently for many years. For this, this paper aims to investigate the practical method of utilizing Siamese models to enhance the accuracy and efficiency of face matching systems. The existing challenges of low accuracy and slow recognition rates in face matching applications have been approved to be solvable by utilizing the capabilities of the Siamese model. Experimental analysis and comments from relevant practitioners demonstrate the effectiveness and potential of the Siamese model in enhancing the performance of face matching systems. To conclude, the Siamese model is introduced as a robust and efficient tool in the field of face recognition. It provides higher accuracy and efficiency compared to the traditional feature-based models. Its adaptability and advancements bring the potential to revolutionize face-matching applications and overcome current limitations. The findings from the experiments demonstrate that the utilization of the joint model can significantly enhance the performance of the matching system. The proposed model offers a potential solution to address the issue of low accuracy during the face matching phase.

Impact of digital transformation on accounting: trends and challenges

The rapid development of the information potential of the digital economic space in Ukraine, the issue of digitalization of the accounting system is becoming of paramount importance are given. The situation is stimulated by the rapid innovation and technological progress and digital transformation of all spheres of social life, as well as the trend of Ukraine's integration into the European economic area. The purpose of the paper is to analyze the current role of digital transformation tools on accounting and to predict the development of its impact in the future. The paper analyzes and assesses the risks caused by the active implementation of digitalization technologies in accounting. The author identifies a number of dangers that will accompany the further rooting of artificial intelligence in information systems, and proposes a number of preventive measures that should be applied within the framework of the general trend of digitalization of accounting. The study was conducted using scientific methods of cognition: abstract and logical and comparative analysis, abstraction, induction and deduction, as well as methods of specification and formalization. It is established that the digital transformation of accounting is positioned as a mandatory optimization stage in the development of the modern business environment. It is substantiated that modern information technologies make it possible to significantly simplify the collection, storage and transfer of accounting information through the introduction of special industry-specific software products. The main digital trends in the process of digitalization of accounting are analyzed, reflecting the vector of the overall development of a sustainable financial environment and the sharing economy. It is proved that the growing use of digital tools makes it possible to make accurate and effective decisions in complex analytical processes. The results of the study may be of practical value for the process of improving the modern accounting system in the context of globalization of digitalization and artificial intelligence technologies. The paper analyzes the feasibility and prospects of applying innovative AI capabilities in the accounting, as well as means and technologies to ensure an appropriate level of security and protection of personal information.

АНАЛИЗ ПРАВОВОЙ ЗАЩИТЫ БЕЗОПАСНОСТИ ПЕРСОНАЛЬНОЙ ИНФОРМАЦИИ В ЭПОХУ БОЛЬШИХ ДАННЫХ

With the advent of the era of big data, the information that people come into contact with on a daily basis is gradually being comprehensively collected and used. Thanks to the use of big data, it is possible to significantly improve the development space of various industries in our country and provide good services to network users. However, when using big data applications, the security of personal information has also been severely affected, and the leakage of personal information is relatively simple. In order to ensure the security of personal information more effectively in the era of big data, this article provides an appropriate analysis of the main characteristics of obtaining and collecting information in the era of big data in combination with the main manifestations of personal information security problems. A comprehensive analysis was also carried out from the point of view of legal protection, and at the end of the article targeted measures were proposed to counteract the legal protection of information security of personal information, in the hope of gradually increasing the level of legal protection of information security of personal information in big data. era.

Personal Information Security Risk Assessment for E-Waste Recycling Based on Fuzzy–GRA–TOPSIS and FMEA

Personal Information Security Risk Assessment for E-Waste Recycling Based on Fuzzy–GRA–TOPSIS and FMEA

The impact of artificial intelligence on Ukrainian medicine: benefits and challenges for the future

The worldwide trend of digitisation across all aspects of society necessitates a fresh approach to the management system in the healthcare sector of Ukraine. The use of artificial intelligence (AI) tools in medicine opens up opportunities for transforming the healthcare sector by creating an effective and personalised approach to medical services. Aims: The purpose of the study was a comprehensive analysis of the current role of artificial intelligence in Ukrainian medicine and the projected future impact of its development. Methodology: The work was completed by utilising several common scientific methods of cognition: logical and structural analysis, abstraction, comparison, induction, and deduction, as well as concretisation and formalisation. Results The benefits resulting from the active implementation of artificial intelligence technologies in medicine were analysed and evaluated in the article. The risks that will accompany the further rooting of AI in medical information systems were identified and a number of appropriate preventive measures were proposed. The experience of developed countries in the transformation of medicine through the use of artificial intelligence capabilities was studied. The feasibility and prospects of using AI technologies in various areas of healthcare were analysed. A number of tools and technologies were proposed to ensure an adequate level of security and protection of personal information. Scientific Novelty: The principles for adapting the existing algorithms of the medical industry in Ukraine to meet the requirements of digitalisation have been formulated, the expediency of transformation in this area was substantiated. Conclusion: It was substantiated that the increasing utilisation of artificial intelligence enables the ability to produce precise and efficient decisions within intricate analytical procedures. The results of the study may be of practical value for the process of optimising the modern medical industry in Ukraine in the context of globalisation of the introduction of artificial intelligence technologies.

Методы кодирования информации на базе трехмерного параллелепипеда на уроках математики

Social informatization requires strong information security measures that would allow companies and individuals to protect their data. Courses in information security may solve this problem. However, methods of coding and encryption can be incorporated in the school curriculum as part of Mathematics and Computer Science. This article introduces an original reliable method of encoding to be integrated with the high school course of Mathematics. The authors used geometric methods of representing points in three-dimensional space bounded by a closed parallelepiped and elementary algebraic operations with matrices. The paper proposes flow and k-gram variants of the matrix method of text encoding and their program implementation in the Python programming language. The encoding methods require no special mathematical training, which makes them teachable to 9–11-graders. By mastering these methods, high-school students can increase their personal information security and develop an algorithmic style of thinking, as well as mathematical skills in general. The article contains some extramural tasks that consolidate practical skills of text coding. The academic and methodological materials make it possible to give the methods of coding and encryption while teaching Mathematics, as well as part of academic disciplines related to information security.