The security of the Industrial Internet of Things (IIoT) is of vital importance, and the Network Intrusion Detection System (NIDS) plays an indispensable role in this. Although there is an increasing number of studies on the use of deep learning technology to achieve network intrusion detection, the limited local data of the device may lead to poor model performance because deep learning requires large-scale datasets for training. Some solutions propose to centralize the local datasets of devices for deep learning training, but this may involve user privacy issues. To address these challenges, this study proposes a novel federated learning (FL)-based approach aimed at improving the accuracy of network intrusion detection while ensuring data privacy protection. This research combines convolutional neural networks with attention mechanisms to develop a new deep learning intrusion detection model specifically designed for the IIoT. Additionally, variational autoencoders are incorporated to enhance data privacy protection. Furthermore, an FL framework enables multiple IIoT clients to jointly train a shared intrusion detection model without sharing their raw data. This strategy significantly improves the model\'s detection capability while effectively addressing data privacy and security issues. To validate the effectiveness of the proposed method, a series of experiments were conducted on a real-world Internet of Things (IoT) network intrusion dataset. The experimental results demonstrate that our model and FL approach significantly improve key performance metrics such as detection accuracy, precision, and false-positive rate (FPR) compared to traditional local training methods and existing models.

BACKGROUND: While the advantages of using the internet and social media for research recruitment are well documented, the evolving online environment also enhances motivations for misrepresentation to receive incentives or to \"troll\" research studies. Such fraudulent assaults can compromise data integrity, with substantial losses in project time; money; and especially for vulnerable populations, research trust. With the rapid advent of new technology and ever-evolving social media platforms, it has become easier for misrepresentation to occur within online data collection. This perpetuation can occur by bots or individuals with malintent, but careful planning can help aid in filtering out fraudulent data.
OBJECTIVE: Using an example with urban American Indian and Alaska Native young women, this paper aims to describe PRIOR (Protocol for Increasing Data Integrity in Online Research), which is a 2-step integration protocol for combating fraudulent participation in online survey research.
METHODS: From February 2019 to August 2020, we recruited participants for formative research preparatory to an online randomized control trial of a preconceptual health program. First, we described our initial protocol for preventing fraudulent participation, which proved to be unsuccessful. Then, we described modifications we made in May 2020 to improve the protocol performance and the creation of PRIOR. Changes included transferring data collection platforms, collecting embedded geospatial variables, enabling timing features within the screening survey, creating URL links for each method or platform of data collection, and manually confirming potentially eligible participants\' identifying information.
RESULTS: Before the implementation of PRIOR, the project experienced substantial fraudulent attempts at study enrollment, with less than 1% (n=6) of 1300 screened participants being identified as truly eligible. With the modified protocol, of the 461 individuals who completed a screening survey, 381 did not meet the eligibility criteria assessed on the survey. Of the 80 that did, 25 (31%) were identified as ineligible via PRIOR. A total of 55 (69%) were identified as eligible and verified in the protocol and were enrolled in the formative study.
CONCLUSIONS: Fraudulent surveys compromise study integrity, validity of the data, and trust among participant populations. They also deplete scarce research resources including respondent compensation and personnel time. Our approach of PRIOR to prevent online misrepresentation in data was successful. This paper reviews key elements regarding fraudulent data participation in online research and demonstrates why enhanced protocols to prevent fraudulent data collection are crucial for building trust with vulnerable populations.
BACKGROUND: ClinicalTrials.gov NCT04376346; https://www.clinicaltrials.gov/study/NCT04376346.
UNASSIGNED: DERR1-10.2196/52281.

Biobanks, through the collection and storage of patient blood, tissue, genomic, and other biological samples, provide unique and rich resources for the research and management of chronic diseases such as cardiovascular diseases, diabetes, and cancer. These samples contain valuable cellular and molecular level information that can be utilized to decipher the pathogenesis of diseases, guide the development of novel diagnostic technologies, treatment methods, and personalized medical strategies. This article first outlines the historical evolution of biobanks, their classification, and the impact of technological advancements. Subsequently, it elaborates on the significant role of biobanks in revealing molecular biomarkers of chronic diseases, promoting the translation of basic research to clinical applications, and achieving individualized treatment and management. Additionally, challenges such as standardization of sample processing, information privacy, and security are discussed. Finally, from the perspectives of policy support, regulatory improvement, and public participation, this article provides a forecast on the future development directions of biobanks and strategies to address challenges, aiming to safeguard and enhance their unique advantages in supporting chronic disease prevention and treatment.

The introduction of OpenAI\'s ChatGPT-4omni (GPT-4o) represents a potential advancement in virtual healthcare and telemedicine. GPT-4o excels in processing audio, visual, and textual data in real time, offering possible enhancements in understanding natural language in both English and non-English contexts. Furthermore, the new \"Temporary Chat\" feature may improve privacy and data confidentiality during interactions, potentially increasing integration with healthcare systems. These innovations promise to enhance communication clarity, facilitate the integration of medical images, and increase data privacy in online consultations. This editorial explores some future implications of these advancements for telemedicine, highlighting the necessity for further research on reliability and the integration of advanced language models with human expertise.

BACKGROUND: Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset\'s utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset\'s utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.
METHODS: Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two.
RESULTS: All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores.
CONCLUSIONS: As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data\'s intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.

UNASSIGNED: Digital technology has the potential to support or infringe upon human rights. The ubiquity of mobile technology in low- and middle-income countries (LMICs) presents an opportunity to leverage mobile health (mHealth) interventions to reach remote populations and enable them to exercise human rights. Yet, simultaneously, the proliferation of mHealth results in expanding sensitive datasets and data processing, which risks endangering rights. The promotion of digital health often centers on its role in enhancing rights and health equity, particularly in LMICs. However, the interplay between mHealth in LMICs and digital rights is underexplored. The objective of this scoping review is to bridge this gap and identify digital rights topics in the 2022 literature on mHealth in Southeast Asian LMICs. Furthermore, it aims to highlight the importance of patient empowerment and data protection in mHealth and related policies in LMICs.
UNASSIGNED: This review follows Arksey and O\'Malley\'s framework for scoping reviews. Search results are reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Frequency and content analyses were applied to summarize and interpret the data.
UNASSIGNED: Three key findings emerge from this review. First, the digital rights topics covered in the literature are sparse, sporadic, and unsystematic. Second, despite significant concerns surrounding data privacy in Southeast Asian LMICs, no article in this review explores challenges to data privacy. Third, all included articles state or allude to the role of mHealth in advancing the right to health.
UNASSIGNED: Engagement in digital rights topics in the literature on mHealth in Southeast Asian mHealth is limited and irregular. Researchers and practitioners lack guidance, collective understanding, and shared language to proactively examine and communicate digital rights topics in mHealth in LMIC research. A systematic method for engaging with digital rights in this context is required going forward.

Differential privacy has emerged as a practical technique for privacy-preserving deep learning. However, recent studies on privacy attacks have demonstrated vulnerabilities in the existing differential privacy implementations for deep models. While encryption-based methods offer robust security, their computational overheads are often prohibitive. To address these challenges, we propose a novel differential privacy-based image generation method. Our approach employs two distinct noise types: one makes the image unrecognizable to humans, preserving privacy during transmission, while the other maintains features essential for machine learning analysis. This allows the deep learning service to provide accurate results, without compromising data privacy. We demonstrate the feasibility of our method on the CIFAR100 dataset, which offers a realistic complexity for evaluation.

BACKGROUND: There is data paucity regarding users\' awareness of privacy concerns and the resulting impact on the acceptance of mobile health (mHealth) apps, especially in the Saudi context. Such information is pertinent in addressing users\' needs in the Kingdom of Saudi Arabia (KSA).
OBJECTIVE: This article presents a study protocol for a mixed method study to assess the perspectives of patients and stakeholders regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA and the factors affecting the adoption of mHealth apps.
METHODS: A mixed method study design will be used. In the quantitative phase, patients and end users of mHealth apps will be randomly recruited from various provinces in Saudi Arabia with a high population of mHealth users. The research instrument will be developed based on the emerging themes and findings from the interview conducted among stakeholders, app developers, health care professionals, and users of mHealth apps (n=25). The survey will focus on (1) how to improve patients\' awareness of data security, privacy, and confidentiality; (2) feedback on the current mHealth apps in terms of data security, privacy, and confidentiality; and (3) the features that might improve data security, privacy, and confidentiality of mHealth apps. Meanwhile, specific sections of the questionnaire will focus on patients\' awareness, privacy concerns, confidentiality concerns, security concerns, perceived usefulness, perceived ease of use, and behavioral intention. Qualitative data will be analyzed thematically using NVivo version 12. Descriptive statistics, regression analysis, and structural equation modeling will be performed using SPSS and partial least squares structural equation modeling.
RESULTS: The ethical approval for this research has been obtained from the Biomedical and Scientific Research Ethics Committee, University of Warwick, and the Medical Research and Ethics Committee Ministry of Health in the KSA. The qualitative phase is ongoing and 15 participants have been interviewed. The interviews for the remaining 10 participants will be completed by November 25, 2023. Preliminary thematic analysis is still ongoing. Meanwhile, the quantitative phase will commence by December 10, 2023, with 150 participants providing signed and informed consent to participate in the study.
CONCLUSIONS: The mixed methods study will elucidate the antecedents of patients\' awareness and concerns regarding the privacy, security, and confidentiality of data collected via mHealth apps in the KSA. Furthermore, pertinent findings on the perspectives of stakeholders and health care professionals toward the aforementioned issues will be gleaned. The results will assist policy makers in developing strategies to improve Saudi users\'/patients\' adoption of mHealth apps and addressing the concerns raised to benefit significantly from these advanced health care modalities.
UNASSIGNED: DERR1-10.2196/54933.

Chronic Obstructive Pulmonary Disease (COPD), as one of the major global health threat diseases, particularly in China, presents a high prevalence and mortality rate. Early diagnosis is crucial for controlling disease progression and improving patient prognosis. However, due to the lack of significant early symptoms, the awareness and diagnosis rates of COPD remain low. Against this background, primary healthcare institutions play a key role in identifying high-risk groups and early diagnosis. With the development of Artificial Intelligence (AI) technology, its potential in enhancing the efficiency and accuracy of COPD screening is evident. This paper discusses the characteristics of high-risk groups for COPD, current screening methods, and the application of AI technology in various aspects of screening. It also highlights challenges in AI application, such as data privacy, algorithm accuracy, and interpretability. Suggestions for improvement, such as enhancing AI technology dissemination, improving data quality, promoting interdisciplinary cooperation, and strengthening policy and financial support, aim to further enhance the effectiveness and prospects of AI technology in COPD screening at primary healthcare institutions in China.

There is a need to transition from conventional (on-site) clinical trials (CTs) to trials conducted within the comfort of a patient\'s home or community (decentralized CT) through e-consent, remote data monitoring, and telemedicine consults. This shift in trial procedures will positively impact recruitment rates, compliance and participant retention, protocol deviations, and delays or missed visits. Home nursing in CTs (HNCTs) will be an important component of this decentralization effort. A few limitations may impact the implementation of HNCT in India. In this regard, the workstream conducted semi-structured qualitative interviews with experts from diverse domains of CT conduct (researchers from academia and industry, clinicians, investigators, nursing staff, patient research advocates, institutional ethics committee, or institutional review board members, legal experts, and trial participants) to collect their understanding, perspectives, and the ground realities about HNCTs in India. The current review puts forth the key areas that would facilitate the establishment of HNCTs in India and provides recommendations for the same.