Personal Information Research Articles

Adherence to melanoma screening and surveillance skin check schedules tailored to personal risk.

Population-wide skin cancer screening is not currently recommended in most countries. Instead, most clinical guidelines incorporate risk-based recommendations for skin checks, despite limited evidence around implementation and adherence to recommendations in practice. We aimed to determine adherence to personal risk-tailored melanoma skin check schedules and explore reasons influencing adherence. Patients (with/without a previous melanoma) attending tertiary dermatology clinics at the Melanoma Institute Australia, Sydney, Australia, were invited to complete a melanoma risk assessment questionnaire via iPad and provided with personal risk information alongside a risk-tailored skin check schedule. Data were collected from the risk tool, clinician-recorded data on schedule deviations, and appointment booking system. Post-consultation, we conducted semi-structured interviews with patients and clinic staff. We used a convergent segregated mixed methods approach for analysis. Interviews were audio recorded, transcribed and data were analysed thematically. Participant data were analysed from clinic records (n = 247) and interviews (n = 29 patients, 11 staff). Overall, there was 62% adherence to risk-tailored skin check schedules. In cases of non-adherence, skin checks tended to occur more frequently than recommended. Decisions to deviate were similarly influenced by patients (44%) and clinicians (56%). Themes driving non-adherence among patients included anxiety and wanting autonomy around decision-making, and among clinicians included concerns around specific lesions and risk estimate accuracy. There was moderate adherence to a clinical service program of personal risk-tailored skin check recommendations. Further adherence may be gained by incorporating strategies to identify and assist patients with high levels of anxiety and supporting clinicians to communicate risk-based recommendations with patients.

Privacy-preserving data mining optimization for big data analytics using deep reinforcement learning

The rapid growth of big data analytics has heightened concerns about data privacy, necessitating the development of advanced privacy-preserving techniques. This research addresses the challenge of optimizing privacy-preserving data mining (PPDM) for big data analytics through the innovative application of deep reinforcement learning (DRL). We propose a novel framework that integrates DRL to dynamically balance privacy and utility, ensuring robust data protection while maintaining analytical accuracy. The framework employs a reinforcement learning agent to adaptively select optimal privacy-preserving strategies based on the evolving data environment and user requirements, while ensuring compliance with the latest security and privacy standards such as ISO/IEC 27001:2023. Experimental results demonstrate significant improvements in both privacy protection and data utility, surpassing traditional PPDM methods. Our findings highlight the potential of DRL in enhancing privacy-preserving mechanisms, offering a scalable and efficient solution for secure big data analytics.

The Relationship Between Sleep Pattern Habits with Severity of Disruptive Behaviors Among Elementary School Children

Objectives: Sleep patterns significantly influence the physical, psychological, and social health of children, with irregularities correlating with behavioral issues like aggression. Studies highlight the association between disrupted sleep pattern and increased aggression in elementary school children, emphasizing the need for interventions promoting healthy sleep habits. This study aims to detect the relationship between sleep pattern habits and disruptive behaviors among children of elementary schools. Methods: A correlational study was conducted in Al-Najaf elementary schools, focusing on disruptive behaviors among 198 children. The study data were collected through a refined Arabic questionnaire and sleep habits scale, revealing severity of disruptive behaviors and personal information variables. Tools’ validity was confirmed by experts, showing high reliability (Cronbach α=0.91). Results: The study examined elementary school children diagnosed with disruptive behaviors in Al-Najaf Province, Iraq. Most participants were male (60.6%), aged 8-10 years (43.9%), and primarily in the first education grade (24.2%). Sleep patterns during weekends and holidays exceeded the normal range (47%), with disruptions reported by over half of the samples (56.1%). No significant correlation was found between disruptive behavior and personal traits, except with variable age, which had a significant correlation (P=0.003). Sleep patterns did not significantly correlate with disruptive behavior, although a weak negative correlation was found between age and education grade. Discussion: Disruptive behaviors are prevalent among elementary school children in Al-Najaf Province, albeit mildly impactful and diminishing with increasing age. According to the current result, these behaviors are not primarily linked to personal traits and show no or little significant correlation with sleep patterns.

A multi-criteria trust-enhanced collaborative filtering algorithm for personalized tourism recommendations

The exponential growth of online information has LED to significant challenges in navigating data overload, particularly in the tourism industry. Travelers are overwhelmed with choices regarding destinations, accommodations, dining, and attractions, making it difficult to select options that best meet their needs. Recommender systems have emerged as a promising solution to this problem, aiding users in decision-making by providing personalized suggestions based on their preferences. Traditional collaborative filtering (CF) methods, however, face limitations, such as data sparsity and reliance on single rating scores, which do not fully capture the complexity of user preferences. This study proposes a hybrid multi-criteria trust-enhanced CF (HMCTeCF) algorithm to improve the accuracy and robustness of tourism recommendations. HMCTeCF improves the quality of recommendations by integrating multi-criteria user preferences with trust relationships among users and between items. Experimental results using real-world datasets, including Restaurants-TripAdvisor and Hotels-TripAdvisor, demonstrate that HMCTeCF outperforms benchmark CF-based recommendation methods. It achieves higher prediction accuracy and coverage rate, effectively addressing the data sparsity problem. This innovative algorithm facilitates a more personalized and enriching travel experience, particularly in scenarios with limited user data. The findings highlight the importance of considering multiple criteria and trust relationships in developing robust recommendation systems for the tourism industry.

Multi platforms fake accounts detection based on federated learning

Identifying and mitigating fake profiles is an urgent issue during the age of widespread integration with social media platforms. this study addresses the challenge of fake profile detection on major social platforms-Facebook, Instagram, and X (Twitter). Employing a two-sided approach, it compares stacking model of machine learning algorithms with the federated learning. The research extends to four datasets, two Instagram datasets, one X dataset, and one Facebook dataset, reporting impressive accuracy metrics. Federated learning stands out for it is effectiveness in fake profile detection, prioritizing user data privacy. Results reveal Instagram fake/real dataset achieves 96% accuracy while Instagram human/bot dataset reaches 95% accuracy with federated learning. using the stacking model X’s fake/real dataset achieves 99.4% accuracy, and Facebook fake/real dataset reaches 99.8% accuracy using the same model. The study underscores the pivotal role of data privacy, positioning federated learning as an ethical choice. It compares the time efficiency of stacking and federated learning, with the former providing good performance in less time and the latter emphasizing data privacy but consuming more time. Results are benchmarked against related works, showcasing superior performance. The study contributes significantly to fake profile detection, offering adaptable solutions and insights.

The intersection of digital safety and financial literacy: Mitigating financial risks in the digital economy

As the digital economy continues to expand, individuals and businesses alike face an increasing range of financial risks, from cybercrime to online fraud. The intersection of digital safety and financial literacy is essential in addressing these risks and ensuring individuals can navigate the complexities of modern financial systems securely. This paper examines the growing need for financial literacy in the context of digital safety, focusing on how individuals can protect themselves from financial threats in the digital economy. It explores the role of digital security measures, such as encryption, secure payment systems, and multi-factor authentication, in safeguarding financial transactions and personal data. Simultaneously, it emphasizes the importance of financial education in equipping individuals with the knowledge and skills to recognize and mitigate online financial risks, such as phishing scams, identity theft, and fraudulent investment schemes. By analysing the current state of digital financial literacy, this paper highlights the gaps in knowledge that leave individuals vulnerable to financial exploitation in the digital space. It also discusses best practices for integrating digital safety education into financial literacy programs, both at the individual and policy levels. Furthermore, the paper outlines the responsibilities of financial institutions, tech companies, and governments in promoting digital safety and financial literacy. Through a multidisciplinary approach, the paper argues that fostering a comprehensive understanding of both digital security and financial management is crucial for mitigating financial risks and enhancing economic resilience in the digital economy.

Assessing the role of artificial intelligence in enhancing customer personalization: A study of ethical and privacy implications in digital marketing

Technological evolution, especially in artificial intelligence (AI), has significantly changed digital marketing, specifically customer-targeted experience. Machine learning, recommendation systems, and predictive analytics have made it possible for enterprises to personalize content, products, and services, among other things, on a very large scale. Nevertheless, all these advances have come with large ethical and privacy concerns that are yet to be met. This paper discusses how AI can enhance customers' personalization to analyze the moral and privacy considerations involved. The research examines how machine learning marketing strategies work in the digital environment and real-life examples of organizations that successfully implement these approaches and potential issues. It highlights how individuals might buy more products and make businesses more profitable with customized advertising techniques. However, the paper also describes the moral problems of AI that use personal data without permission, artificially intelligent algorithms that affect decision-making, and artificial intelligence decision-making. At the same time, its work has yet to be well known. The paper looks into privacy risks like data breach incidents, privacy violations, GDPR, and CCPA. This paper adopts an exploratory case study research design as it gathers academic papers, industry examples, and expert opinions to present a balanced view of the effects of AI. Based on the research, conclusions can be drawn that great opportunities AI opens for personalization should be met with safety considerations and working ethical guidelines. In the end, the paper presents guidelines that should help businesses integrate trust and compliance into creating and using AI technologies.

A Secure and Transparent Voting System using Blockchain Technology

Blockchain-based voting systems present a transformative approach to resolving the limitations found in conventional voting methods. Traditional electoral processes frequently suffer from issues like vote manipulation, fraud, uncertainty, and inefficiencies in vote counting. By leveraging the decentralized nature of blockchain technology, these challenges can be mitigated, creating a secure, unchangeable, and transparent platform for vote recording and tallying. Once a vote is registered on the blockchain, it becomes unalterable, safeguarding election results against any form of tampering or external manipulation. Each vote is stored as a cryptographic entry, chained to the preceding data, which fortifies the overall security of the voting record against unauthorized modifications. This structure upholds the integrity of the election data, ensuring robust control over the electoral process. With the blockchain ledger publicly accessible, all participants—including voters and candidates—can verify results in real time, promoting confidence in the accuracy of the voting process. The technology's decentralized framework eliminates the need for centralized oversight, reducing the risks of manipulation or corruption by third parties. Additionally, this approach maintains voter privacy, allowing individuals to cast their votes confidentially without fear of personal information exposure or reprisal. Smart contracts within the system automate essential functions such as voter registration, identity validation, and vote casting, minimizing human errors and enabling seamless election processes. In conclusion, adopting blockchain technology in voting systems has the potential to greatly enhance security, transparency, and trust, thereby supporting and strengthening democratic practices on a global scale.

The Power of TaaS: Challenges and Considerations of TaaS Adoption

Telecom as a Service (TaaS) is an emerging model based upon cloud infrastructure supporting telecommunications functions more cost-effectively and flexibly. The growth of TaaS adoption is explored in this research, and the major benefits to its adoption by telecom operators, including reduced cost, scalability, and perfect service delivery, are identified. However, organizations have to face several challenges when adopting TaaS. Security, regulatory compliance, and data privacy are all key issues. TaaS is a security vulnerability and compliance that is particularly complex across different legal jurisdictions, as it runs in a cloud environment. Also, safeguarding user data on privacy regulations remains a big deal. We discuss these challenges and propose strategies to overcome them so that telecom customers can have secure, compliant, and private TaaS.

Design Guidelines for Improving Mobile Sensing Data Collection: Prospective Mixed Methods Study

Background Machine learning models often use passively recorded sensor data streams as inputs to train machine learning models that predict outcomes captured through ecological momentary assessments (EMA). Despite the growth of mobile data collection, challenges in obtaining proper authorization to send notifications, receive background events, and perform background tasks persist. Objective We investigated challenges faced by mobile sensing apps in real-world settings in order to develop design guidelines. For active data, we compared 2 prompting strategies: setup prompting, where the app requests authorization during its initial run, and contextual prompting, where authorization is requested when an event or notification occurs. Additionally, we evaluated 2 passive data collection paradigms: collection during scheduled background tasks and persistent reminders that trigger passive data collection. We investigated the following research questions (RQs): (RQ1) how do setup prompting and contextual prompting affect scheduled notification delivery and the response rate of notification-initiated EMA? (RQ2) Which authorization paradigm, setup or contextual prompting, is more successful in leading users to grant authorization to receive background events? and (RQ3) Which polling-based method, persistent reminders or scheduled background tasks, completes more background sessions? Methods We developed mobile sensing apps for iOS and Android devices and tested them through a 30-day user study asking college students (n=145) about their stress levels. Participants responded to a daily EMA question to test active data collection. The sensing apps collected background location events, polled for passive data with persistent reminders, and scheduled background tasks to test passive data collection. Results For RQ1, setup and contextual prompting yielded no significant difference (ANOVA F1,144=0.0227; P=.88) in EMA compliance, with an average of 23.4 (SD 7.36) out of 30 assessments completed. However, qualitative analysis revealed that contextual prompting on iOS devices resulted in inconsistent notification deliveries. For RQ2, contextual prompting for background events was 55.5% (χ21=4.4; P=.04) more effective in gaining authorization. For RQ3, users demonstrated resistance to installing the persistent reminder, but when installed, the persistent reminder performed 226.5% more background sessions than traditional background tasks. Conclusions We developed design guidelines for improving mobile sensing on consumer mobile devices based on our qualitative and quantitative results. Our qualitative results demonstrated that contextual prompts on iOS devices resulted in inconsistent notification deliveries, unlike setup prompting on Android devices. We therefore recommend using setup prompting for EMA when possible. We found that contextual prompting is more efficient for authorizing background events. We therefore recommend using contextual prompting for passive sensing. Finally, we conclude that developing a persistent reminder and requiring participants to install it provides an additional way to poll for sensor and user data and could improve data collection to support adaptive interventions powered by machine learning.

The utility of personal wearable data in long COVID and personalized patient care

The utility of personal wearable data in long COVID and personalized patient care

New technologies in the case law of the Court of Justice – lessons learnt and lessons to be learnt

Abstract The article identifies the most important legal challenges related to new technologies on the basis of the existing case law of the Court of Justice. The author analyses how these experiences make us re-examine established legal concepts. The discussion focuses on issues such as the principle of territoriality, the protection of personal data, the role of the Internet platform, and liability for infringements committed via the internet. In addition, the author seeks to identify what challenges still lie ahead for courts and legislators.

Examining the Relationships between Career Development, Daily Life Decision Making and Social Emotional Learning Skills of Middle School Students

This presented research has three purposes: The first purpose of this study is to examine the power of middle school students' social emotional learning and daily life decision-making skills together as a predictor of their career development. The second purpose is to examine the career development of middle school students according to gender. Third purpose is to examine the career development of middle school students according to targeted high school type. The research group consisted of 263 middle school students (141 female, 116 male, 6 unknown). These students were studying in 3 middle schools in the center of a large city in the northwestern part of Turkey and their ages ranged between 10-15 (X̄=12.31; sd=1.07). The research data were collected by applying the Chilhood Career Development Scale, Social Emotional Learning Scale for Adolescents, Daily Life Decision Making Scale and Personal Information Form. The research data were analysed using multiple linear regression analysis, t test, and one-way ANOVA. The research results showed that middle school students' social emotional learning and daily life decision-making skills scores together accounted for a significant proportion of the variance 0.19% of the career development scores of middle school students. The results showed that the career development score mean of students targeted Anatolian high school were higher than those aiming for science high school and vocational and technical high school. There was not significantly differences in career development scores of the students according to gender. The research results were discussed based on the relevant literature, provided important implications for the theoretical and practical literature on career development, social emotional learning and daily life decision-making skills and suggestions were presented for future theoretical and practical research.

Crack Detection and Cost Analyzer Using AI

Crack Vision is an innovative AI-powered Android application developed to detect and analyze wall cracks, providing users with real-time, automated repair cost estimates. By leveraging advanced object detection models such as YOLOv7 and Mask R-CNN, Crack Vision enhances both the accuracy and efficiency of crack detection, enabling precise measurement of crack dimensions, severity assessment, and quick identification of structural issues. This application simplifies the inspection and budgeting process for wall repairs, making it an essential tool for homeowners, contractors, and structural engineers who seek to minimize reliance on manual inspection methods. The core functionality of Crack Vision combines high-resolution image processing with state-of-the-art machine learning algorithms, ensuring reliable detection across various wall types and crack textures. Using computer vision and deep learning techniques, the app can differentiate between structural and non-structural cracks, thereby offering a comprehensive solution for repair prioritization. Furthermore, Crack Vision integrates with a robust backend that stores user data, allowing users to track the condition of their walls over time. This feature aids in preventive maintenance by identifying trends in crack expansion and deterioration, potentially mitigating costly repairs. Additionally, the application provides a detailed breakdown of estimated repair costs based on crack size, depth, and type, empowering users to make informed decisions about necessary repairs. Crack Vision is designed with user-friendliness in mind, featuring an intuitive interface that guides users through the inspection process. The app also supports multilingual options and customizable settings to cater to diverse user needs. In sum, Crack Vision redefines wall crack inspection, offering a rapid, accurate, and accessible solution for both casual users and industry professionals, ultimately contributing to improved building safety and maintenance efficiency

Secure Search of Smart Home Data Based on Access Control Encryption

Abstract: In recent years, the increasing variety of smart home devices, such as smart locks, surveillance cameras, and various sensors, has led to a significant rise in related data. To facilitate the sharing of smart home data, manufacturers store these data on the Internet of Things (IoT). However, since IoT servers are not fully trustworthy and the data often contain sensitive commercial information, any leakage could result in a commercial crisis for manufacturers. To address security concerns, manufacturers encrypt the data before storing them on the IoT. A challenge arises: how can users search for target data in encrypted form? This paper proposes a new search encryption scheme for smart home data, addressing the limitations of existing attribute-based encryption (ABE) searchable encryption schemes. The proposed scheme integrates access control trees and bilinear mapping technology, supporting multiple access policies including AND gates, OR gates, and threshold gates. Experimental results demonstrate a comprehensive analysis of the attribute-based searchable encryption protocol under a multi-authority architecture, covering four dimensions: correctness, security, complexity, and real-world application scenarios. In specific scenarios, when a data user, out of privacy concerns, is unwilling to reveal all attribute information to a single authority during a query for shared datathus avoiding identity disclosureor when the users identity information is managed by multiple independent authorities, this protocol proves highly effective. It ensures smooth data sharing while maintaining data security for both data owners and users, fully safeguarding data privacy and shareability.

Social technologies in management of modern Russian society

The article attempts to characterize the problem field of scientific research of the Department of Social Technologies of the Sociological Faculty of Moscow State University as part of the study of social technologies in the management of modern Russian society, with an emphasis on the technology of modernization of the Russian society and transformation of management practices in the context of new external challenges (primarily related to the active introduction of digital information and communication technologies).Modern Russian society is developing in the context of external and internal challenges requiring the transformation of management practices and the development of new social technologies. In connection with the need to implement the task set by the authorities of choosing a way of further development and rethinking the system of spiritual and moral values, the problem of the development, selection and application of social technologies of socio-cultural, economic and political modernization of Russian society has been actualized. Looking retrospectively at the process of modernization of Russia, one can clearly see the “pendulum” of development — in the direction of borrowing “Western” experience and implementing reforms by western standards or, on the contrary, “return to the roots” and implementing counter-reforms. In the current geopolitical situation, we can state that there has been a demand not only for a return to traditional values and conservative ideology, but also for a rethinking of Russia as a “distinct civilization”.Serious challenges (socio-demographic changes, digitalization, algorithmization, robotization, development of artificial intelligence, machine learning, shering economy, etc.) facing society and its social institutions today, require rethinking management practices and social technologies. The digitalization of social management is increasingly challenging the identification and analysis of the social consequences of introducing artificial intelligence, replacing human labor with machine and algorithms. When we talk about changing the nature of management in a digitalized society, we see an increasing penetration of artificial intelligence technologies into the field of management and the development of algorithmic control. Algorithms are now used in public administration, social services and social services, education, health. The autonomy and autonomization of algorithmic solutions raise serious questions about reducing the role of the human factor in management, which is alarming, especially in spheres that have traditionally been built around the human being. In addition, machines do not just study a person, they try to push him to make certain decisions, to a given behavioral response to a given stimulus, open new possibilities for manipulation. With the active introduction of digital technologies in all spheres of public life and in the management system, there are fears of total digital control. As digital platforms gain more instrumental power and access to personal data, so does the resistance of users of digital platforms due to a lack of control over the “fate” of the data provided. All this can lead to an increase in social tensions, which cannot be ignored when implementing management practices and choosing modern social technologies of management of society.

RFL-LSU: A Robust Federated Learning Approach with Localized Stepwise Updates

Distributed intelligence enables the widespread deployment of AI technology, greatly promoting the development of AI. Federated learning is a widely used distributed intelligence technology that allows iterative optimization of global model while protecting user data privacy. Currently, federated learning faces some security threats, as its open architecture provides attackers with opportunities to disrupt the learning process by submitting malicious updates or inserting backdoors. In this article, we propose a robust federated learning method to defend against potential malicious attacks. Specifically, we enhance the algorithm’s performance and stability by implementing localized stepwise updates on the client side and element-wise anomaly detection on the server side. We conducted experiments in a more realistic non-i.i.d. scenario and compared the results with other typical federated learning methods. Our results demonstrate that our approach exhibits strong robustness under non-i.i.d. data distributions, outperforming other methods in terms of test accuracy and resilience to attacks.

Personal Data in the Digital Age - Technosophy and Information Law

Personal Data in the Digital Age - Technosophy and Information Law

Kolmogorov-Arnold Representation Based Informed Orchestration in Solving Partial Differential Equations for Carbon Balance Sustainability

This paper introduces the Kolmogorov-Arnold Credit Informed Network Orchestration (KACINO), a novel framework proposed to comprehensively structure and analyze the carbon dynamic system. By synthesizing dynamic processes such as carbon emission and sequestration, a credit information of carbon dynamics is built accumulation, KACINO provides a holistic approach to model the complexities inherent in carbon dynamics. Through systematic analysis and scenario simulations, KACINO facilitates informed policy recommendations aimed at optimizing carbon credit utilization and achieving sustainable environmental outcomes. This paper outlines the theoretical foundation, methodology, and practical applications of KACINO, highlighting its potential to support transformative strategies in climate change mitigation and sustainable development.

Robots and humans working together: factors influencing job pursuit in sustainable diverse hospitality environments

ABSTRACT This study aims to investigate the effects of personal innovativeness, information sharing, environmental, social, and governance sustainability, sustainable diversity, valence, and instrumentality on job pursuit intention in diverse hospitality environments where robots and humans collaborate, comparing prospective White and non-White employees. Drawing on sustainable diversity and inclusion theory, a framework incorporating these factors was developed and tested using Generalised Linear Modelling (GLM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). GLM and FsQCA revealed that six independent variables are necessary for high job pursuit intention in White and non-White groups, while the two groups had notable similar but different configuration requirements regarding diversity and technology.