Personal Information Research Articles

Privacy protection against user profiling through optimal data generalization

Personalized information systems are information-filtering systems that endeavor to tailor information-exchange functionality to the specific interests of their users. The ability of these systems to profile users based on their search queries at Google, disclosed locations at Twitter or rated movies at Netflix, is on the one hand what enables such intelligent functionality, but on the other, the source of serious privacy concerns. Leveraging on the principle of data minimization, we propose a data-generalization mechanism that aims to protect users’ privacy against non-fully trusted personalized information systems. In our approach, a user may like to disclose personal data to such systems when they feel comfortable. But when they do not, they may wish to replace specific and sensitive data with more general and thus less sensitive data, before sharing this information with the personalized system in question. Generalization therefore may protect user privacy to a certain extent, but clearly at the cost of some information loss. In this work, we model mathematically an optimized version of this mechanism and investigate theoretically some key properties of the privacy-utility trade-off posed by this mechanism. Experimental results on two real-world datasets demonstrate how our approach may contribute to privacy protection and show it can outperform state-of-the-art perturbation techniques like data forgery and suppression by providing higher utility for a same privacy level. On a practical level, the implications of our work are diverse in the field of personalized online services. We emphasize that our mechanism allows each user individually to take charge of their own privacy, without the need to go to third parties or share resources with other users. And on the other hand, it provides privacy designers/engineers with a new data-perturbative mechanism with which to evaluate their systems in the presence of data that is likely to be generalizable according to a certain hierarchy, highlighting spatial generalization, with practical application in popular location based services. Overall, a data-perturbation mechanism for privacy protection against user profiling, which is optimal, deterministic, and local, based on a untrusted model towards third parties.

Development of Mathematical Algorithm for Detecting XSS Attacks on Web Applications

The widespread usage of web applications has led to an increase in security threats, with Cross-Site Scripting (XSS) attacks being one of the most prevalent and damaging. Detecting and mitigating XSS attacks is crucial to ensure the integrity and confidentiality of sensitive user data. This article presents the mathematical algorithm and a way to identify XSS attacks using a bounded function from below, which depends on the input string, and highlights its potential impact in bolstering web application security. To construct this function, we used special characters and keywords that are frequently found in the construction of XSS attacks.

Sci-ModoM: a quantitative database of transcriptome-wide high-throughput RNA modification sites.

We present Sci-ModoM, the first next-generation RNome database offering a holistic view of the epitranscriptomic landscape. Sci-ModoM has a simple yet powerful interface, underpinned by FAIR data principles, a standardized nomenclature, and interoperable formats, fostering the use of common standards within the epitranscriptomics community. Sci-ModoM provides quantitative measurements per site and dataset, enabling users to assess confidence levels based on score, coverage, and stoichiometry. Data in Sci-ModoM is directly traceable to its sources. Users can Search and Compare over six million modifications across 156 datasets, Browse or download datasets, and retrieve metadata. A comparison tool offers a novel and unique opportunity to compare modifications site-wise across datasets, with the ability to securely upload and compare user data against latest published research. Sci-ModoM empowers researchers, including non-experts, to access a broad spectrum of recent quantitative RNA modification data, thereby enhancing the utility and impact of latest discoveries, and opening new avenues in biological and medical research.

Securing privacy in the digital age: The quest for judicial protection of personal information

Personal information is a vital productive commodity in the digital economy, and its processing has seen unparalleled transformations in both breadth and depth. This article proposes to enhance the legal remedies for personal information rights in contemporary China. Research has revealed multiple practical challenges in China’s judicial practices, such as hesitance to prosecute owing to an absence of substantial legal foundation, improper distribution of the burden of proof, and inadequate integration of criminal-civil judicial safeguards for personal information. This paper advocates for China to elucidate the definition of personal information rights via legislation, enable the litigation of personal information infringement cases, and establish explicit criteria for their acceptance into judicial proceedings. Furthermore, China must develop an appropriate structure for distributing the burden of evidence. It must also use discretionary judgment to properly tackle the problems related to evaluating damages in instances of personal information violations.

Disparities in Patient Portal Activation and Usage at a Large Pediatric Academic Institution.

Access to personal medical information promotes patient understanding of health issues and enables patient self-advocacy of healthcare needs. The advent of electronic medical record systems and the 2016 21st Century CURES Act promoted and encouraged patient access to personal medical information, yet technology-dependent modalities have often disadvantaged certain communities. We sought to evaluate whether disparities existed in access to patient portals at our institution, the main pediatric care provider in an area serving one million children. We evaluated the activation of patient portal accounts at our institution over the past decade (2010-2021). Portal activation data were analyzed by ethnic background and language preference and income information available based on primary home access location. Further, we evaluated portal activation rates over time and across institutional interventions. A logistic regression model was used to identify important demographic associations with portal account status. Over 1 million patients were served at our pediatric institution over the study period with 47.7% having ever activated their patient portals. Univariate analyses and ultimately logistic regression modeling demonstrated significant differences in portal activation by ethnicity (odds ratio (confidence interval):1.36 (1.34, 1.37)), language preference (1.39 (1.37, 1.40)), and median household income (1.00001 (1.00001, 1.00001)). Interim interventions were successful in improving portal activation rates. Overall, electronic medical record portal activation was less prevalent among Hispanic, non-English speakers, and patients living in communities with lower median household income. Opportunities for interventions exist to reduce healthcare disparities in these underserved communities.

Learning face super-resolution through identity features and distilling facial prior knowledge

Deep learning techniques in electronic surveillance have shown impressive performance for super-resolution (SR) of captured low-quality face images. Most of these techniques adopt facial priors to improve the feature details in the resultant super-resolved images. However, the estimation of facial priors from the captured low-quality images is often inaccurate in real-life situations because of their tiny, noisy, and blurry nature. Thus, the fusion of such priors badly affects the performance of these models. Therefore, this work presents a teacher–student-based face SR framework that efficiently preserves the personal facial structure information in the super-resolved faces. In the proposed framework, the teacher network exploits the facial heatmap-based ground-truth-prior to learn the facial structure that is utilized by the student network. The student network is trained with the identity feature loss for maintaining the identity and facial structure information in reconstructed high-resolution (HR) face images. The performance of the proposed framework is evaluated by conducting the experimental study on standard datasets namely CelebA-HQ and LFW face. The experimental results reveal that the proposed technique conquers the existing methods for the face SR task.

Elevating citizen engagement in the process of territorial development through digitisation

The purpose of the study is to examine the digitisation of the state system involving local executive bodies to identify effective strategies for engaging citizens in territorial development decision-making. The following methods were applied in the study: hermeneutic, statistical, structural-functional, and comparative methods. During the study, the main problems associated with the implementation of digital technologies in the territorial development system involving the country's citizens were examined, such as technological barriers, issues of confidentiality and data security, and uneven distribution of resources. This allowed formulating recommendations for problem resolution, namely, ensuring access to education, developing digital literacy among the population, devising effective measures to protect data, and guaranteeing the confidentiality of personal information. The paper also explores the prospects and threats to the economies of regions in the process of transitioning to a digital economy. Special attention is given to studying the indicators of the current state of digitisation in Kazakhstan, considering data from annual international ratings. The obtained research results can be used in the development and implementation of digital technologies in the territory management system at the international-regional level for the benefit of citizens.

CAPP: Context‐Aware Privacy‐Preserving Continuous Authentication in Smartphones

ABSTRACTThe pervasive use of smart devices in everyday life has increased the risk of theft and cyberattacks, necessitating robust and continuous behavioral authentication techniques during every interactive session. Traditional authentication methods are insufficient to address these evolving threats, as they often fail to maintain security throughout the session. To address this challenge, we propose a continuous authentication scheme that leverages touch and motion sensors, incorporating user context to enhance security. However, building an effective authentication model requires creating a behavioral template by aggregating data from multiple users. Moreover, outsourcing sensitive sensor data to third‐party servers for processing introduces the risk of privacy breaches, such as behavioral profiling or the theft of personal information. To mitigate these risks, we designed CAPP—a context‐aware privacy‐preserving data outsourcing scheme for continuous authentication during live sessions. CAPP employs Format Preserving Encryption (FPE) to encrypt the sensor data, ensuring that the data's format remains intact, which allows the machine learning model to function with minimal loss of accuracy. Our solution enables secure and efficient authentication by creating a model on the server side using data from motion sensors like the accelerometer, gyroscope, and magnetometer. Our experimental results demonstrate the effectiveness of the proposed scheme, achieving an accuracy of 87.57% and an Equal Error Rate (EER) of 12.15%, while requiring only 1.69 ms to authenticate a user. These results show that CAPP preserves user privacy and provides a rapid and reliable authentication process, outperforming existing state‐of‐the‐art methods.

Determining the relationship between gynecologic cancer awareness and health literacy among women of reproductive age: a descriptive study.

To determine the relationship between gynecologic cancer awareness and health literacy among women of reproductive age. This study was conducted in a descriptive design and involved 510 women. Data collection utilized the "Personal Information Form", "Gynecologic Cancer Awareness Scale (GCAS)" and "Health Literacy Scale (HLS)", gathered. Pearson correlation and linear regression analyses, independent samples t-test, one-way analysis of variance and post hoc analyses were employed for data analysis. Effect sizes were calculated using eta-squared (η2) coefficients. The mean total score of women on the GCAS was 154.42 ± 18.27, while the mean total score on the HLS was 57.07 ± 7.86. Significant differences were observed in women's GCAS total scores concerning age, perceived income status, and history of gynecological examination, whereas significant differences were noted in HLS total scores concerning age, educational status, marital status, and perceived income status. A weak positive correlation was detected betwixt women's GCAS and HLS total scores. It was determined that 20.8% of the total variation in gynecologic cancer awareness level was explained by health literacy. Women exhibit an above-average level of awareness regarding gynecologic cancers. Health literacy level contributes to an increase in gynecologic cancer awareness.

Perceived Social Support and Depression, Anxiety and Stress in Pregnant Women Diagnosed With Foetal Anomaly.

To examine the relationship between perceived social support level and depression, anxiety and stress in pregnant women diagnosed with foetal anomaly. This descriptive and correlational study was conducted in an advanced prenatal evaluation unit of a university hospital between December 2021 and May 2022. The study data collected from 131 pregnant women through a personal information form, depression, anxiety and stress scale (DASS-42) and multidimensional scale of perceived social support (MSPSS). Most of the pregnant women were in the second trimester of pregnancy, and more than half had been advised by a healthcare professional to terminate their pregnancy. Overall, the pregnant women reported moderate levels of social support, while their depression, anxiety and stress levels varied. There was a weak negative correlation between perceived social support from family, friends and multidimensional sources with stress, but the effect rate was low. Most pregnant women diagnosed with foetal anomaly have normal levels of depression, stress and anxiety. There is a weak negative correlation between perceived social support and stress, with family and friend support affecting stress levels at a low rate. Professional support should be provided, and both the woman's mental health and social support mechanisms must be evaluated. This study highlights the importance of social support in managing stress among pregnant women with foetal anomalies. While most women had normal levels of depression, anxiety and stress, increased social support from family and friends was shown to reduce stress. The findings underscore the need for healthcare professionals to assess and strengthen mental health and social support systems in this vulnerable population, informing interventions to improve psychosocial outcomes. This descriptive and correlational study adhered to the CONSORT guidelines for reporting non-randomised trials. No patient or public contribution.

Ethical considerations related to personal data collection and reuse: trust and transparency in language and speech technologies

Ethical considerations related to personal data collection and reuse: trust and transparency in language and speech technologies

Marketing Management in Ed-tech: Innovations and Challenges in Promoting Educational Technologies

In the rapidly evolving field of educational technologies (Ed-tech), effective marketing management is crucial for promoting innovations and engaging a diverse audience. This research explores the use of AI-driven predictive analytics to optimize marketing strategies in the Ed-tech industry. By harnessing vast amounts of user data, predictive models are employed to identify emerging trends, anticipate customer needs, and personalize marketing content. This approach enhances the relevance of promotional efforts and improves conversion rates. However, the application of AI in this domain also poses challenges, such as ensuring data privacy, managing biases in predictive models, and balancing personalization with ethical marketing practices. The research provides insights into the innovations AI brings to Ed-tech marketing while addressing the challenges that need to be navigated to fully leverage its potential. This framework offers a strategic blueprint for Ed-tech companies to stay competitive in a crowded marketplace.

The hidden emotions of therapists: An autoethnographic exploration of working with clients who self‐injure

AbstractIntroductionThis paper explores how working with clients who self‐injure generates significant emotional reactions in therapists, often difficult to manage. Drawing on my clinical experience as a researcher and counsellor, I provide an evocative autoethnographic account, highlighting hidden, forbidden or taboo feelings. The aim was to deepen understanding of therapists' emotional realities and contribute to the limited literature on this subject.MethodThrough journaling, sketching, metaphor and field notes, I detail personal experiences with clients who self‐injure. This autoethnography explores my emotions and reactions. A case vignette illustrates my experiences, promoting critical and empathic consideration of how therapist emotions are experienced.Data AnalysisI use autoethnographic methods to analyse the emotional impact and existential reflections of working with clients who self‐injure, employing layers of qualitative interpretation from various personal data sources.ResultsFindings reveal that working with clients who self‐injure forces me to confront my mortality, evoking deep existential reflections and intense emotions like vulnerability and fear. This disrupts my sense of immortality, highlighting my role's limitations and evoking shame and self‐doubt about my ability to alleviate suffering.Implications for PracticeThis paper advances research on self‐injury and emphasises autoethnography as a valuable avenue for counsellors engaging in research. Grounded in PhD study, this paper makes an original contribution to knowledge. Integrating discussions on mortality and emotional vulnerability into supervision and training is crucial, alongside comprehensive training that addresses emotional and unconscious issues. Accessible supervision fosters growth, reduces stigma and supports therapists working with clients who self‐injure.

OPTIMIZING AUTHENTICATION SECURITY IN INTELLIGENT SYSTEMS THROUGH VISUAL BIOMETRICS FOR ENHANCED EFFICIENCY

Context. The primary objective of this article is to explore aspects related to ensuring security and enhancing the efficiency of authentication processes in intelligent systems through the application of visual biometrics. The focus is on advancing and refining authentication systems by employing sophisticated biometric identification methods. Objective. A specialized intelligent system has been developed, utilizing a Siamese neural network to establish secure user authentication within the existing system. Beyond incorporating fundamental security measures such as hashing and secure storage of user credentials, the contemporary significance of implementing two-factor authentication is underscored. This approach significantly fortifies user data protection, thwarting most contemporary hacking methods and safeguarding against data breaches. The study acknowledges certain limitations in its approach, possibly affecting the generalizability of the findings. These limitations provide avenues for future research and exploration, contributing to the ongoing evolution of authentication methodologies in intelligent systems. Method. The two-factor authentication system integrates facial recognition technology, employing visual biometrics for heightened security compared to alternative two-factor authentication methods. Various implementations of the Siamese neural network, utilizing Contrastive loss function and Triplet loss function, were evaluated. Subsequently, a neural network employing the Triplet loss function was implemented and trained. Results. The article emphasizes the practical implications of the developed intelligent system, showcasing its effectiveness in minimizing the risk of unauthorized access to user accounts. The integration of contemporary authentication methodologies ensures a secure and robust user authentication process. Conclusions. The implementation of facial recognition technology in authentication processes has broader social implications. It contributes to a more secure digital environment by preventing unauthorized access, ultimately safeguarding user privacy and data. The study’s originality lies in its innovative approach to authentication, utilizing visual biometrics within a Siamese neural network framework. The developed intelligent system represents a valuable contribution to the field, offering an effective and contemporary solution to user authentication challenges.

O IMPACTO DA INTELIGÊNCIA ARTIFICIAL NA SOCIEDADE DIANTE DA LEI GERAL DE PROTEÇÃO DE DADOS: A LIMITAÇÃO JURÍDICA DA INTELIGÊNCIA ARTIFICIAL DIANTE DA LEI Nº 13.709/18

This is a scientific article whose theme is the impact of artificial intelligence on society in light of the General Data Protection Law. The LGPD defines the liability of data processing agents, who are controllers and operators, for damages caused by the exercise of the processing activity, in a manner similar to the system of the Consumer Defense Code (CDC). By providing citizens with rights over their data, the LGPD also tends to increase public transparency regarding data collection and analysis. Thus, it will not be restricted to private companies; in the public sphere, the State will also have to be clearer about how it handles citizen data. The aforementioned law establishes rules for the storage, sharing, use, and collection of citizen data, whether by public or private companies. The article aims to demonstrate how the uncontrolled use of artificial intelligence affects society as a whole, such as the new personal data protection legislation, protecting users and democracy, as well as highlighting the main difficulties faced by companies in the process of adapting and complying with the LGDP standards and the legal limitations of Artificial Intelligence. As a theoretical basis, articles, conventions and laws that corroborate the Brazilian legal system were adopted. The methodology used was a descriptive and exploratory literature review.

The Three-Tier Structural Legal Deficit Undermining the Protection of Employees’ Personal Data in the Workplace

The Three-Tier Structural Legal Deficit Undermining the Protection of Employees’ Personal Data in the Workplace

CONSERVATION AND ARCHIVING MEDICAL RECORDS IN SELECTED HOSPITALS IN YAOUNDÉ

Introduction: Medical records play an important role in the storage and recall of patient’s information, patient’s follow-up, research, teaching, public health policy making and as a medico-legal tool, but less emphasis has been placed on its role in health care delivery. The objective of the study was to appraise the methods of conservation and archiving of medical records in hospitals in the city of Yaoundé.Methodology: A descriptive cross-sectional study was done in the hospitals of Yaoundé between January to June 2023. The Data which was collected from medical record clerks using a self-administered and pre-tested questionnaire included: The profile of the health institution, personal information of the clerks, strategies put in place for the protection of medical records and obstacles in archiving.Results: Twenty hospitals in the city of Yaoundé participated in the study. The public sector represented 65% of the health facilities, 45% had been existing between 21 and 40 years, with a median of 24.5 years. In 70% of the cases, medical records archiving system had been existing between 1 and 10years. In 65% of cases, the conservation of archived records was mainly done at the different services with the paediatric service being the most represented (16.2%). In 87.5% of cases, the means of archiving used was the paper file classified on shelves (73.5%); of which (52.2%) were archived in alphabetical order. The obstacles to archiving medical records were mainly the lack of qualified personnel (82.4%) and the lack of storage space (80.1%).Conclusion: Many obstacles were observed. These results suggest actions to be generated both by the Cameroonian State and by the leaders of private health facilities in order to improve the management of medical records.

Privacy and personal data risk governance for generative artificial intelligence: A Chinese perspective

The rapid development of generative artificial intelligence (AI) has attracted global attention and posed challenges to existing data governance frameworks. The increased technical complexity and expanded scale of data usage not only make it more difficult to regulate AI but also present challenges for the current legal system. This article, which takes ChatGPT's training data and working principles as a starting point, examines specific privacy risks, data leakage risks, and personal data risks posed by generative AI. It also analyzes the latest practices in privacy and personal data protection in China. This article finds that while China's governance on privacy and personal data protection takes a macro-micro integration approach and a private-and-public law integration approach, there are shortcomings in the legal system. Given that the current personal data protection system centered on individual control is unsuitable for the modes of data processing by generative AI, and that private law is insufficient in safeguarding data privacy, urgent institutional innovation is needed to achieve the objective of “trustworthy AI.”

Identifying critical elements in using Question Prompt Lists at the pharmacy counter to induce patient activation—using principles of conversation analysis

BackgroundThe incorporation of Question Prompt Lists (QPLs) into pharmacy interactions has been tested as an innovative strategy aimed at enhancing patient engagement and addressing personal information needs. However, there is a gap in understanding regarding how QPLs induce or reduce patient activation and contribute to improved medical treatment. The specific aim of the study was therefore to qualitatively describe how pharmacy encounters in which QPL are introduced unfold, in order to identify and discuss relevant interactional mechanisms that induce or reduce patient activation. MethodsThe QPL, which includes questions about usage, interaction, side effects, and follow-up, was introduced to patients during pharmacy encounters. Employing a qualitative inductive approach based on principles from conversation analysis (CA), audio-recordings from 56 QPL encounters in community pharmacies were transcribed and thematically analysed. ResultsMost meetings began with along initiation phase focused on determining which medications to take home. This was followed by an introduction to the QPL and a natural break where the pharmacist left to get the medicines, giving the patient an opportunity to contemplate their needs, as prompted by the QPL. While the QPL itself was not explicitly explained or discussed, the majority of patients asked questions and shared personal information. Even those who were disinterested felt compelled to justify their stance, demonstrating the influence of the norm of reciprocity. However, instances of unsolicited information provision by the pharmacist, were still identified. ConclusionThis study demonstrates that Question Prompt Lists (QPLs) can enhance patient activation in pharmacy encounters, even without detailed explanations, but their impact is not straightforward. The presence of the QPL encouraged patients to engage, share personal information, and ask questions. These findings suggest that QPLs can create opportunities for patient involvement, even in brief pharmacy interactions.

Shadow footprints and the provision of digital behavioral data

We introduce the concept of shadow footprints as a means of understanding privacy challenges in the information environment. Growing emphasis on the impact of citizens sharing their personal data goes far beyond the individual, with the increased capacity of algorithms to formulate shadow footprints that inform as much about persons absent from data, as persons present. Data extrapolated from small groups have demonstrated robust utility when applied to larger populations. Individuals who have opted to keep their data private, or who have been unaware that data about their private lives has been extracted from the involvement of their fellow citizens from digital behavioral data, suggest the possibility that informed consent has been circumvented, or not fully investigated. This is increasingly concerning when one considers the potential to impact the body politic through behavioral manipulation drawn from such data. These issues must be considered in the context of ethical and litigious standards to inform robust policy frameworks, legal regulation, and the provision of incentives necessary to provide guidance and civic protections, as well as adherence to good ethical practice.