Dignity: the cornerstone of psychological health among victims of child labor, a qualitative study.

BACKGROUND: Respecting the dignity of child labor is one of their most urgent needs. In many cases, the dignity of child labor is not maintained in countries with unfavorable economic conditions. The aim of the present study was understand adolescents' perceptions of their dignity in child labor. METHODS: This study is a qualitative research with conventional content analysis approach. Twenty teenagers who having work experience as child labor were selected from one welfare center and three charity centers in using purposeful sampling method in 2022-2023. Data was generated through individual, deep, and semi-structured interviews. In order to analyze the data was used Granheim and Lundman's method. RESULTS: Three main themes were presented in this study including, "preservation of privacy and security", "honoring individual identity to develop dignity" and "comprehensive support", and 9 categories. CONCLUSION: understand adolescents' perceptions as child labor of their dignity, privacy and security of child labor victims and respect for their identity and all-round support are defined. And in this supportive environment, the dignity of working children is preserved and appropriate behavioral consequences are created. Therefore, it is suggested that a cultural and institutional background be provided in which all components of the child labor's dignity are emphasized.

Federated unsupervised random forest for privacy-preserving patient stratification.

MOTIVATION: In the realm of precision medicine, effective patient stratification and disease subtyping demand innovative methodologies tailored for multi-omics data. Clustering techniques applied to multi-omics data have become instrumental in identifying distinct subgroups of patients, enabling a finer-grained understanding of disease variability. Meanwhile, clinical datasets are often small and must be aggregated from multiple hospitals. Online data sharing, however, is seen as a significant challenge due to privacy concerns, potentially impeding big data's role in medical advancements using machine learning. This work establishes a powerful framework for advancing precision medicine through unsupervised random forest-based clustering in combination with federated computing. RESULTS: We introduce a novel multi-omics clustering approach utilizing unsupervised random forests. The unsupervised nature of the random forest enables the determination of cluster-specific feature importance, unraveling key molecular contributors to distinct patient groups. Our methodology is designed for federated execution, a crucial aspect in the medical domain where privacy concerns are paramount. We have validated our approach on machine learning benchmark datasets as well as on cancer data from The Cancer Genome Atlas. Our method is competitive with the state-of-the-art in terms of disease subtyping, but at the same time substantially improves the cluster interpretability. Experiments indicate that local clustering performance can be improved through federated computing. AVAILABILITY AND IMPLEMENTATION: The proposed methods are available as an R-package (https://github.com/pievos101/uRF).

Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces.

Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

PatCen: A blockchain-based patient-centric mechanism for the granular access control of infectious disease-related test records.

The recent global outbreaks of infectious diseases such as COVID-19, yellow fever, and Ebola have highlighted the critical need for robust health data management systems that can rapidly adapt to and mitigate public health emergencies. In contrast to traditional systems, this study introduces an innovative blockchain-based Electronic Health Record (EHR) access control mechanism that effectively safeguards patient data integrity and privacy. The proposed approach uniquely integrates granular data access control mechanism within a blockchain framework, ensuring that patient data is only accessible to explicitly authorized users and thereby enhancing patient consent and privacy. This system addresses key challenges in healthcare data management, including preventing unauthorized access and overcoming the inefficiencies inherent in traditional access mechanisms. Since the latency is a sensitive factor in healthcare data management, the simulations of the proposed model reveal substantial improvements over existing benchmarks in terms of reduced computing overhead, increased throughput, minimized latency, and strengthened overall security. By demonstrating these advantages, the study contributes significantly to the evolution of health data management, offering a scalable, secure solution that prioritizes patient autonomy and privacy in an increasingly digital healthcare landscape.

Privacy preserving technology in ophthalmology.

PURPOSE OF REVIEW: Patient privacy protection is a critical focus in medical practice. Advances over the past decade in big data have led to the digitization of medical records, making medical data increasingly accessible through frequent data sharing and online communication. Periocular features, iris, and fundus images all contain biometric characteristics of patients, making privacy protection in ophthalmology particularly important. Consequently, privacy-preserving technologies have emerged, and are reviewed in this study. RECENT FINDINGS: Recent findings indicate that general medical privacy-preserving technologies, such as federated learning and blockchain, have been gradually applied in ophthalmology. However, the exploration of privacy protection techniques of specific ophthalmic examinations, like digital mask, is still limited. Moreover, we have observed advancements in addressing ophthalmic ethical issues related to privacy protection in the era of big data, such as algorithm fairness and explainability. SUMMARY: Future privacy protection for ophthalmic patients still faces challenges and requires improved strategies. Progress in privacy protection technology for ophthalmology will continue to promote a better healthcare environment and patient experience, as well as more effective data sharing and scientific research.

Ethical assessment of virtual consultation services: application of a practical ethical checklist to direct-to-consumer services in Aotearoa New Zealand.

Introduction Evaluating digital health services from an ethical perspective remains one of the more difficult challenges in telemedicine and health technology assessment. We have previously developed a practical ethical checklist comprising 25 questions covering six ethical themes: privacy, security, and confidentiality; equity; autonomy and informed consent; quality and standards of care; patient empowerment; and continuity of care. The checklist makes ethical analysis more easily accessible to a broader audience, including health care providers, technology developers, and patients. Aim This project applies the previously developed practical ethical checklist to direct-to-consumer virtual primary care consultation services in Aotearoa New Zealand to conduct an ethical assessment. Method We first mapped the available services. The ethical framework was then applied to assess these services based on publicly available information. Results Our findings show that the examined virtual consultation services adequately address ethical considerations, particularly regarding patient data privacy and informed consent. We identified areas for improvement in equity, patient empowerment, and continuity of care. Discussion The application of this framework raises fundamental questions on how continuity of care, equity, and comprehensive care can be protected when virtual care becomes more ubiquitous. The checklist can help virtual consultation services identify areas of improvement and ensure they meet ethical criteria, thus contributing to quality of care. The framework may be adapted to other digital health services and tools, providing ethical guidance to technology developers, clinicians, and patients and their whanau (family).

Prioritizing Technology Initiatives to Reduce Social Isolation and Loneliness Among Older Adults.

In their insightful commentary, Kokorelias et al. (2024) explore the potential of technology in supporting aging in the right place, addressing both opportunities and challenges from individual to societal levels. Our commentary specifically focuses on recent empirical evidence for technology's benefits in enhancing social connectivity and reducing loneliness for older adults, both with and without cognitive impairments. It emphasizes the need for a proper balance between the use of technology and face-to-face interactions and highlights the importance of addressing concerns related to privacy, cybersecurity and safety in this domain. In addition to the barriers outlined by Kokorelias et al. (2024), we discuss challenges related to the transfer of technology, the necessary steps required to ensure that technological interventions are effective beyond well-controlled studies and the responsibility of industries to design technology in such a way that innovations can benefit as many people as possible.

A location-based service scheme with attribute information privacy.

In location-based service (LBS), private information retrieval (PIR) is an efficient strategy used for preserving personal privacy. However, schemes with traditional strategy that constructed by information indexing are usually denounced by its processing time and ineffective in preserving the attribute privacy of the user. Thus, in order to cope with above two weaknesses, in this paper, based on the conception of ciphertext policy attribute-based encryption (CP-ABE), a PIR scheme based on CP-ABE is proposed for preserving the personal privacy in LBS (location privacy preservation scheme with CP-ABE based PIR, short for LPPCAP). In this scheme, query and feedback are encrypted with security two-parties calculation by the user and the LBS server, so as not to violate any personal privacy and decrease the processing time in encrypting the retrieved information. In addition, this scheme can also preserve the attribute privacy of users such as the query frequency as well as the moving manner. At last, we analyzed the availability and the privacy of the proposed scheme, and then several groups of comparison experiment are given, so that the effectiveness and the usability of proposed scheme can be verified theoretically, practically, and the quality of service is also preserved.

Privacy Risk Assessment for Synthetic Longitudinal Health Data.

INTRODUCTION: A modern approach to ensuring privacy when sharing datasets is the use of synthetic data generation methods, which often claim to outperform classic anonymization techniques in the trade-off between data utility and privacy. Recently, it was demonstrated that various deep learning-based approaches are able to generate useful synthesized datasets, often based on domain-specific analyses. However, evaluating the privacy implications of releasing synthetic data remains a challenging problem, especially when the goal is to conform with data protection guidelines. METHODS: Therefore, the recent privacy risk quantification framework Anonymeter has been built for evaluating multiple possible vulnerabilities, which are specifically based on privacy risks that are considered by the European Data Protection Board, i.e. singling out, linkability, and attribute inference. This framework was applied to a synthetic data generation study from the epidemiological domain, where the synthesization replicates time and age trends previously found in data collected during the DONALD cohort study (1312 participants, 16 time points). The conducted privacy analyses are presented, which place a focus on the vulnerability of outliers. RESULTS: The resulting privacy scores are discussed, which vary greatly between the different types of attacks. CONCLUSION: Challenges encountered during their implementation and during the interpretation of their results are highlighted, and it is concluded that privacy risk assessment for synthetic data remains an open problem.

Studying Privacy Aspects of Learned Knowledge Bases in the Context of Synthetic and Medical Data.

INTRODUCTION: Retrieving comprehensible rule-based knowledge from medical data by machine learning is a beneficial task, e.g., for automating the process of creating a decision support system. While this has recently been studied by means of exception-tolerant hierarchical knowledge bases (i.e., knowledge bases, where rule-based knowledge is represented on several levels of abstraction), privacy concerns have not been addressed extensively in this context yet. However, privacy plays an important role, especially for medical applications. METHODS: When parts of the original dataset can be restored from a learned knowledge base, there may be a practically and legally relevant risk of re-identification for individuals. In this paper, we study privacy issues of exception-tolerant hierarchical knowledge bases which are learned from data. We propose approaches for determining and eliminating privacy issues of the learned knowledge bases. RESULTS: We present results for synthetic as well as for real world datasets. CONCLUSION: The results show that our approach effectively prevents privacy breaches while only moderately decreasing the inference quality.

A bidirectional reversible and multilevel location privacy protection method based on attribute encryption.

Various methods such as k-anonymity and differential privacy have been proposed to safeguard users' private information in the publication of location service data. However, these typically employ a rigid "all-or-nothing" privacy standard that fails to accommodate users' more nuanced and multi-level privacy-related needs. Data is irrecoverable once anonymized, leading to a permanent reduction in location data quality, in turn significantly diminishing data utility. In the paper, a novel, bidirectional and multi-layered location privacy protection method based on attribute encryption is proposed. This method offers layered, reversible, and fine-grained privacy safeguards. A hierarchical privacy protection scheme incorporates various layers of dummy information, using an access structure tree to encrypt identifiers for these dummies. Multi-level location privacy protection is achieved after adding varying amounts of dummy information at different hierarchical levels N. This allows for precise control over the de-anonymization process, where users may adjust the granularity of anonymized data based on their own trust levels for multi-level location privacy protection. This method includes an access policy which functions via an attribute encryption-based access control system, generating decryption keys for data identifiers according to user attributes, facilitating a reversible transformation between data anonymity and de-anonymity. The complexities associated with key generation, distribution, and management are thus markedly reduced. Experimental comparisons with existing methods demonstrate that the proposed method effectively balances service quality and location privacy, providing users with multi-level and reversible privacy protection services.

Navigating ethical challenges in online wildlife trade research.

The surge in internet accessibility has transformed wildlife trade by facilitating the acquisition of wildlife through online platforms. This scenario presents unique ethical challenges for researchers, as traditional ethical frameworks for in-person research cannot be readily applied to the online realm. Currently, there is a lack of clearly defined guidelines for appropriate ethical procedures when conducting online wildlife trade (OWT) research. In response to this, we consulted the scientific literature on ethical considerations in online research and examined existing guidelines established by professional societies and ethical boards. Based on these documents, we present a set of recommendations that can inform the development of ethically responsible OWT research. Key ethical challenges in designing and executing OWT research include the violation of privacy rights, defining subjects and illegality, and the risk of misinterpretation or posing risks to participants when sharing data. Potential solutions include considering participants' expectations of privacy, defining when participants are authors versus subjects, understanding the legal and cultural context, minimizing data collection, ensuring anonymization, and removing metadata. Best practices also involve being culturally sensitive when analyzing and reporting findings. Adhering to these guidelines can help mitigate potential pitfalls and provides valuable insights to editors, researchers, and ethical review boards, enabling them to conduct scientifically rigorous and ethically responsible OWT research to advance this growing field.

Los retos éticos de la investigación del mercado virtual de fauna Resumen El incremento en el acceso al internet ha transformado el mercado de fauna ya que facilita la adquisición de ejemplares a través de plataformas virtuales. Este escenario representa un reto ético único para los investigadores, pues los marcos éticos tradicionales para la investigación en persona no pueden aplicarse fácilmente en línea. Actualmente no hay lineamientos claros para el procedimiento ético apropiado cuando se investiga el mercado virtual de fauna (MVF). Como respuesta, consultamos la literatura científica sobre las consideraciones éticas en la investigación en línea y analizamos los lineamientos existentes establecidos por las sociedades profesionales y los comités éticos. Con base en estos documentos, presentamos un conjunto de recomendaciones que pueden guiar el desarrollo de la investigación sobre el MVF con responsabilidad ética. Los retos más importantes para el diseño y ejecución de la investigación sobre el MVF incluyen la violación del derecho a la privacidad, la definición de los sujetos y la ilegalidad y el riesgo de malinterpretar o presentar riesgos para los participantes cuando se comparten datos. Las soluciones potenciales incluyen considerar las expectativas de privacidad de los participantes, definir cuándo los participantes son autores y cuándo sujetos, entender el contexto legal y cultural, minimizar la recolección de datos, asegurar el anonimato y eliminar los metadatos. Las mejores prácticas también involucran la sensibilidad cultural cuando se analizan y reportan los resultados. La adhesión a estos lineamientos puede mitigar los posibles retos y proporcionar información valiosa para los editores, investigadores y comités de ética, permitiéndoles realizar una investigación con rigor científico y responsabilidad ética sobre el MVF para avanzar en este campo creciente de investigación.

Factors influencing recommendation intentions for autonomous vehicles: A path analysis in a pilot study.

Over the past decade, the rapid development of artificial intelligence has propelled the transition of autonomous vehicles from laboratories to real-world applications. However, autonomous vehicles are a long way from fully integrating into most people's lives. Previous studies indicate that the word-of-mouth effect is often used by consumers to determine the quality of innovative technologies. Word-of-mouth recommendation can not only increase the income of enterprises by attracting new customers, but also greatly reduce the promotion and publicity expenses of enterprises. Through the word-of-mouth effect, the intention to recommend can contribute to the growth of the autonomous driving market. Therefore, current research explores the mechanisms among the perceived risk of privacy safety, perceived defect, perceived behavioral control, intention to use, and intention to recommend through path analysis. Our findings, based on 433 online questionnaires, indicate that the perceived risk of privacy safety, perceived defects, and perceived behavioral control influence the intention to recommend. Notably, perceived risk of privacy safety and perceived defect directly affects the intention to recommend and also correlates with perceived behavioral control. These findings provide some empirical evidence for the recommendation of autonomous vehicles and the expansion of consumer groups.

Subgraph-level federated graph neural network for privacy-preserving recommendation with meta-learning.

Graph neural networks (GNN) are widely used in recommendation systems, but traditional centralized methods raise privacy concerns. To address this, we introduce a federated framework for privacy-preserving GNN-based recommendations. This framework allows distributed training of GNN models using local user data. Each client trains a GNN using its own user-item graph and uploads gradients to a central server for aggregation. To overcome limited data, we propose expanding local graphs using Software Guard Extension (SGX) and Local Differential Privacy (LDP). SGX computes node intersections for subgraph exchange and expansion, while local differential privacy ensures privacy. Additionally, we introduce a personalized approach with Prototype Networks (PN) and Model-Agnostic Meta-Learning (MAML) to handle data heterogeneity. This enhances the encoding abilities of the federated meta-learner, enabling precise fine-tuning and quick adaptation to diverse client graph data. We leverage SGX and local differential privacy for secure parameter sharing and defense against malicious servers. Comprehensive experiments across six datasets demonstrate our method's superiority over centralized GNN-based recommendations, while preserving user privacy.

Exploring the Depths of Sharenting: Unveiling the Impact of Sociodemographic Factors and Internet Addiction.

INTRODUCTION: This study explored sharenting's impact on children's privacy and factors influencing parental sharing. Limited knowledge raises concerns about children's rights in this growing phenomenon. METHOD: A quasi-experimental cross-sectional study included 411 parents (372 females, 39 males) with a mean age of 38.5 ± 10.5 years. Chi-square tests analyzed group differences; regression assessed the "sharenting practice" impact. RESULTS: Out of 411 parents, 67.2% (n = 247) shared photographs of their children on social media, whereas 32.8% (n = 164) did not share. Significant associations were found between sharenting and factors such as younger age (B = -0.06, p = .002), lower bachelor's degree level (B = 0.87, p < .001), higher internet addiction (B = 0.05, p < .001), and longer social media use (B = 0.17, p < .001). DISCUSSION: Understanding factors in sharenting's impact on children's rights is crucial. Our findings suggest sociodemographic factors, internet addiction, and social media duration influence sharenting. Health professionals can guide parents on responsible social media usage and digital literacy to protect their children's online privacy.

Inter-relationships among individual views of COVID-19 control measures across multi-cultural contexts.

Individual-level georeferenced data have been widely used in COVID-19 control measures around the world. Recent research observed that there is a trade-off relationship between people's privacy concerns and their acceptance of these control measures. However, whether this trade-off relationship exists across different cultural contexts is still unaddressed. Using data we collected via an international survey (n = 4260) and network analysis, our study found a substantial trade-off inter-relationship among people's privacy concerns, perceived social benefits, and acceptance across different control measures and study areas. People's privacy concerns in culturally tight societies (e.g., Japan) have the smallest negative impacts on their acceptance of pandemic control measures. The results also identify people's key views of specific control measures that can influence their views of other control measures. The impacts of these key views are heightened among participants with a conservative political view, high levels of perceived social tightness, and vertical individualism. Our results indicate that cultural factors are a key mechanism that mediate people's privacy concerns and their acceptance of pandemic control measures. These close inter-relationships lead to a double-edged sword effect: the increased positive impacts of people's acceptance and perceived social benefits also lead to increased negative impacts of privacy concerns in different combinations of control strategies. The findings highlight the importance of cultural factors as key determinants that affect people's acceptance or rejection of specific pandemic control measures.

"I was given PrEP, but had no privacy": Mystery shopper perspectives of PrEP counseling for adolescent girls and young women in Kisumu County, Kenya.

Pre-exposure prophylaxis (PrEP) is being scaled up to prevent HIV acquisition among adolescent girls and young women (AGYW) in Eastern and Southern Africa. In a prior study more than one-third of AGYW 'mystery shoppers' stated they would not return to care based on interactions with health providers. We examined the experiences of AGYW in this study to identify main barriers to effective PrEP services. Unannounced patient actors (USP/'mystery shoppers') posed as AGYWs seeking PrEP using standardized scenarios 8 months after providers had received training to improve PrEP services. We conducted targeted debriefings using open-ended questions to assess PrEP service provision and counseling quality with USPs immediately following their visit. Debriefings were audio-recorded and transcribed. Transcripts were analyzed using thematic analysis to explore why USPs reported either positive or negative encounters. We conducted 91 USP debriefings at 24 facilities and identified three primary influences on PrEP service experiences: 1) Privacy improved likelihood of continuing care, 2) respectful attitudes created a safe environment for USPs, and 3) patient-centered communication improved the experience and increased confidence for PrEP initiation among USPs. Privacy and provider attitudes were primary drivers that influenced decision-making around PrEP in USP debriefs. Access to privacy and improving provider attitudes is important for scale-up of PrEP to AGYW.

MolCFL: A personalized and privacy-preserving drug discovery framework based on generative clustered federated learning.

In today's era of rapid development of large models, the traditional drug development process is undergoing a profound transformation. The vast demand for data and consumption of computational resources are making independent drug discovery increasingly difficult. By integrating federated learning technology into the drug discovery field, we have found a solution that both protects privacy and shares computational power. However, the differences in data held by various pharmaceutical institutions and the diversity in drug design objectives have exacerbated the issue of data heterogeneity, making traditional federated learning consensus models unable to meet the personalized needs of all parties. In this study, we introduce and evaluate an innovative drug discovery framework, MolCFL, which utilizes a multi-layer perceptron (MLP) as the generator and a graph convolutional network (GCN) as the discriminator in a generative adversarial network (GAN). By learning the graph structure of molecules, it generates new molecules in a highly personalized manner and then optimizes the learning process by clustering federated learning, grouping compound data with high similarity. MolCFL not only enhances the model's ability to protect privacy but also significantly improves the efficiency and personalization of molecular design. MolCFL exhibits superior performance when handling non-independently and identically distributed data compared to traditional models. Experimental results show that the framework demonstrates outstanding performance on two benchmark datasets, with the generated new molecules achieving over 90% in Uniqueness and close to 100% in Novelty. MolCFL not only improves the quality and efficiency of drug molecule design but also, through its highly customized clustered federated learning environment, promotes collaboration and specialization in the drug discovery process while ensuring data privacy. These features make MolCFL a powerful tool suitable for addressing the various challenges faced in the modern drug research and development field.

How users make online privacy decisions in work and personal contexts of use.

With the rising usage of contactless work options since COVID-19, users increasingly share their personal data in digital tools at work. Using an experimental online vignette study (N = 93), we examined users' willingness to use a video conferencing tool, while systematically varying the context of use (personal vs. low trustworthiness work vs. high trustworthiness work) and the type of information shared (low vs. medium vs. high sensitivity). We also assessed users' perceived responsibility in work and personal contexts of use and their self-assessed digital competence. Our results highlight employer trustworthiness as an important factor in the willingness to use a third-party video conferencing tool, with increased willingness to use these tools in work contexts of use with high trustworthiness compared to those with low trustworthiness. This effect seems to be reduced when the data to be shared is of high sensitivity, compared to medium and low sensitivity data. Furthermore, despite reduced responsibility for data protection in work compared to personal contexts of use, the willingness to use a video conferencing tool did not decrease between trustworthy work and personal contexts of use. We discuss our findings and their methodological implications for future research and derive implications for privacy decisions at work.

On the evaluation of synthetic longitudinal electronic health records.

BACKGROUND: Synthetic Electronic Health Records (EHRs) are becoming increasingly popular as a privacy enhancing technology. However, for longitudinal EHRs specifically, little research has been done into how to properly evaluate synthetically generated samples. In this article, we provide a discussion on existing methods and recommendations when evaluating the quality of synthetic longitudinal EHRs. METHODS: We recommend to assess synthetic EHR quality through similarity to real EHRs in low-dimensional projections, accuracy of a classifier discriminating synthetic from real samples, performance of synthetic versus real trained algorithms in clinical tasks, and privacy risk through risk of attribute inference. For each metric we discuss strengths and weaknesses, next to showing how it can be applied on a longitudinal dataset. RESULTS: To support the discussion on evaluation metrics, we apply discussed metrics on a dataset of synthetic EHRs generated from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) repository. CONCLUSIONS: The discussion on evaluation metrics provide guidance for researchers on how to use and interpret different metrics when evaluating the quality of synthetic longitudinal EHRs.