A federated learning architecture for secure and private neuroimaging analysis.

The amount of biomedical data continues to grow rapidly. However, collecting data from multiple sites for joint analysis remains challenging due to security, privacy, and regulatory concerns. To overcome this challenge, we use federated learning, which enables distributed training of neural network models over multiple data sources without sharing data. Each site trains the neural network over its private data for some time and then shares the neural network parameters (i.e., weights and/or gradients) with a federation controller, which in turn aggregates the local models and sends the resulting community model back to each site, and the process repeats. Our federated learning architecture, MetisFL, provides strong security and privacy. First, sample data never leave a site. Second, neural network parameters are encrypted before transmission and the global neural model is computed under fully homomorphic encryption. Finally, we use information-theoretic methods to limit information leakage from the neural model to prevent a "curious" site from performing model inversion or membership attacks. We present a thorough evaluation of the performance of secure, private federated learning in neuroimaging tasks, including for predicting Alzheimer's disease and for brain age gap estimation (BrainAGE) from magnetic resonance imaging (MRI) studies in challenging, heterogeneous federated environments where sites have different amounts of data and statistical distributions.

Prompt Engineering Paradigms for Medical Applications: Scoping Review.

BACKGROUND: Prompt engineering, focusing on crafting effective prompts to large language models (LLMs), has garnered attention for its capabilities at harnessing the potential of LLMs. This is even more crucial in the medical domain due to its specialized terminology and language technicity. Clinical natural language processing applications must navigate complex language and ensure privacy compliance. Prompt engineering offers a novel approach by designing tailored prompts to guide models in exploiting clinically relevant information from complex medical texts. Despite its promise, the efficacy of prompt engineering in the medical domain remains to be fully explored. OBJECTIVE: The aim of the study is to review research efforts and technical approaches in prompt engineering for medical applications as well as provide an overview of opportunities and challenges for clinical practice. METHODS: Databases indexing the fields of medicine, computer science, and medical informatics were queried in order to identify relevant published papers. Since prompt engineering is an emerging field, preprint databases were also considered. Multiple data were extracted, such as the prompt paradigm, the involved LLMs, the languages of the study, the domain of the topic, the baselines, and several learning, design, and architecture strategies specific to prompt engineering. We include studies that apply prompt engineering-based methods to the medical domain, published between 2022 and 2024, and covering multiple prompt paradigms such as prompt learning (PL), prompt tuning (PT), and prompt design (PD). RESULTS: We included 114 recent prompt engineering studies. Among the 3 prompt paradigms, we have observed that PD is the most prevalent (78 papers). In 12 papers, PD, PL, and PT terms were used interchangeably. While ChatGPT is the most commonly used LLM, we have identified 7 studies using this LLM on a sensitive clinical data set. Chain-of-thought, present in 17 studies, emerges as the most frequent PD technique. While PL and PT papers typically provide a baseline for evaluating prompt-based approaches, 61% (48/78) of the PD studies do not report any nonprompt-related baseline. Finally, we individually examine each of the key prompt engineering-specific information reported across papers and find that many studies neglect to explicitly mention them, posing a challenge for advancing prompt engineering research. CONCLUSIONS: In addition to reporting on trends and the scientific landscape of prompt engineering, we provide reporting guidelines for future studies to help advance research in the medical field. We also disclose tables and figures summarizing medical prompt engineering papers available and hope that future contributions will leverage these existing works to better advance the field.

A Smart Water Bottle and Companion App (HidrateSpark 3) to Improve Bladder-Filling Compliance in Patients With Prostate Cancer Receiving Radiotherapy: Nonrandomized Trial of Feasibility and Acceptability.

BACKGROUND: Patients with prostate cancer undergoing radiation therapy (RT) need comfortably full bladders to reduce toxicities during treatment. Poor compliance is common with standard of care written or verbal instructions, leading to wasted patient value (PV) and clinic resources via poor throughput efficiency (TE). OBJECTIVE: Herein, we assessed the feasibility and acceptability of a smartphone-based behavioral intervention (SBI) to improve bladder-filling compliance and methods for quantifying PV and TE. METHODS: In total, 36 patients with prostate cancer were enrolled in a single-institution, closed-access, nonrandomized feasibility trial. The SBI consists of a fully automated smart water bottle and smartphone app. Both pieces alert the patient to empty his bladder and drink a personalized volume goal, based on simulation bladder volume, 1.25 hours before his scheduled RT. Patients were trained to adjust their volume goal and notification times to achieve comfortably full bladders. The primary end point was met if qualitative (QLC) and quantitative compliance (QNC) were >80%. For QLC, patients were asked if they prepared their bladders before daily RT. QNC was met if bladder volumes on daily cone-beam tomography were >75% of the simulation's volume. The Service User Technology Acceptability Questionnaire (SUTAQ) was given in person pre- and post-SBI. Additional acceptability and engagement end points were met if >3 out of 5 across 4 domains on the SUTAQ and >80% (15/18) of patients used the device >50% of the time, respectively. Finally, the impact of SBI on PV and TE was measured by time spent in a clinic and on the linear accelerator (linac), respectively, and contrasted with matched controls. RESULTS: QLC was 100% in 375 out of 398 (94.2%) total treatments, while QNC was 88.9% in 341 out of 398 (85.7%) total treatments. Of a total score of 5, patients scored 4.33 on privacy concerns, 4 on belief in benefits, 4.56 on satisfaction, and 4.24 on usability via SUTAQ. Further, 83% (15/18) of patients used the SBI on >50% of treatments. Patients in the intervention arm spent less time in a clinic (53.24, SEM 1.71 minutes) compared to the control (75.01, SEM 2.26 minutes) group (P<.001). Similarly, the intervention arm spent less time on the linac (10.67, SEM 0.40 minutes) compared to the control (14.19, SEM 0.32 minutes) group (P<.001). CONCLUSIONS: This digital intervention trial showed high rates of bladder-filling compliance and engagement. High patient value and TE were feasibly quantified by shortened clinic times and linac usage, respectively. Future studies are needed to evaluate clinical outcomes, patient experience, and cost-benefit. TRIAL REGISTRATION: ClinicalTrials.gov NCT04946214; https://www.clinicaltrials.gov/study/NCT04946214.

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.

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers. AIM: The study aims to diagnose a patient's pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer. METHODS: Machine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process. RESULTS: Among the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features "plasma_CA19_9", REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process. CONCLUSIONS: This research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.

Regulatory Challenges and Frameworks for Fog Computing in Healthcare.

The integration of fog computing into healthcare promises significant advancements in real-time data analytics and patient care by decentralizing data processing closer to the source. This shift, however, introduces complex regulatory, privacy, and security challenges that are not adequately addressed by existing frameworks designed for centralized systems. The distributed nature of fog computing complicates the uniform application of security measures and compliance with diverse international regulations, raising concerns about data privacy, security vulnerabilities, and legal accountability. This review explores these challenges in depth, discussing the implications of fog computing's decentralized architecture for data privacy, the difficulties in achieving consistent security across dispersed nodes, and the complexities of ensuring compliance in multi-jurisdictional environments. It also examines specific regulatory frameworks, including Health Insurance Portability and Accountability (HIPAA) in the United States, General Data Protection Regulation (GDPR) in the European Union, and emerging laws in Asia and Brazil, highlighting the gaps and the need for regulatory evolution to better accommodate the nuances of fog computing. The review advocates for a proactive regulatory approach, emphasizing the development of specific guidelines, international collaboration, and public-private partnerships to enhance compliance and support innovation. By embedding privacy and security by design and leveraging advanced technologies, healthcare providers can navigate the regulatory landscape effectively, ensuring that fog computing realizes its full potential as a transformative healthcare technology without compromising patient trust or data integrity.

BYOD security behaviour and preferences among hospital clinicians - A qualitative study.

BACKGROUND/OBJECTIVE: The use of personal devices for work purposes (Bring-your-own-device) has increased in hospitals, as it facilitates productivity and mobility for clinicians. However, owing to increased risk of leaking patient information, and heavy reliance of patient data privacy on user actions, BYOD is a major challenge for hospitals. There has been a dearth of empirical research studying clinicians' BYOD security behaviour. Therefore, the study's aim was to attain subjective understanding of clinicians' attitudes and preferences towards protecting patient data on their devices through a qualitative study. METHODS: 14 semi-structured interviews were conducted among Australian hospital-based clinicians. A hybrid thematic analysis was conducted using the framework method to explore socio-technical themes pertaining to the clinicians' BYOD security behavioural practices. RESULTS: Limited use of secure tools like antivirus and passcodes, and inadequate separation of patient and personal data on BYOD devices was found. Key technology concerns included malware introduction into hospital network, inadvertent patient data sharing, and slow remote access. Hospitals lacked dedicated BYOD policies and training, resulting in unsafe practices. Participants also cited misalignment of BYOD policies with workflow needs, privacy maintenance challenges and fears of personal data breaches, while calling for improved communication between technical and clinical staff and a strong cybersecurity culture. CONCLUSION: This study provides a comprehensive understanding of BYOD related user behaviour and the usefulness of security controls used in time-sensitive and complex hospital environments. It can inform future policies or processes by advocating for secure and productive BYOD use.

Equitable differential privacy.

Differential privacy (DP) has been in the public spotlight since the announcement of its use in the 2020 U.S. Census. While DP algorithms have substantially improved the confidentiality protections provided to Census respondents, concerns have been raised about the accuracy of the DP-protected Census data. The extent to which the use of DP distorts the ability to draw inferences that drive policy about small-populations, especially marginalized communities, has been of particular concern to researchers and policy makers. After all, inaccurate information about marginalized populations can often engender policies that exacerbate rather than ameliorate social inequities. Consequently, computer science experts have focused on developing mechanisms that help achieve equitable privacy, i.e., mechanisms that mitigate the data distortions introduced by privacy protections to ensure equitable outcomes and benefits for all groups, particularly marginalized groups. Our paper extends the conversation on equitable privacy by highlighting the importance of inclusive communication in ensuring equitable outcomes for all social groups through all the stages of deploying a differentially private system. We conceptualize Equitable DP as the design, communication, and implementation of DP algorithms that ensure equitable outcomes. Thus, in addition to adopting computer scientists' recommendations of incorporating equity parameters within DP algorithms, we suggest that it is critical for an organization to also facilitate inclusive communication throughout the design, development, and implementation stages of a DP algorithm to ensure it has an equitable impact on social groups and does not hinder the redressal of social inequities. To demonstrate the importance of communication for Equitable DP, we undertake a case study of the process through which DP was adopted as the newest disclosure avoidance system for the 2020 U.S. Census. Drawing on the Inclusive Science Communication (ISC) framework, we examine the extent to which the Census Bureau's communication strategies encouraged engagement across the diverse groups of users that employ the decennial Census data for research and policy making. Our analysis provides lessons that can be used by other government organizations interested in incorporating the Equitable DP approach in their data collection practices.

A new unified measure for evaluation of randomized response techniques.

Randomized response scrambling techniques have been in existence for over fifty years. These scrambling methods are very useful in sample surveys where researchers deal with sensitive variables. Out of many available scrambling techniques, survey researchers often need to evaluate these techniques to choose the best technique for real-world surveys. In the current literature, only a limited number of model-evaluation metrics are available for analyzing the performance of different scrambling methods. This leaves a big research gap for the development of new unified evaluation measures which can quantify all aspects of a scrambling technique. We develop a novel unified metric for evaluation of randomized response models and compare it with the existing unified measure. The proposed measure can quantify the efficiency and the level of the respondents' privacy of any scrambling technique. Being less sensitive to sample sizes than the existing unified measure, the proposed measure can be used with small sample sizes to evaluate models.

Navigating the crossroads of aging, caregiving and technology: Insights from a southern Spain about video-based technology in the care context.

Objective: As the world faces an aging population, the complexities of care management become increasingly pronounced. While technological solutions hold promise in addressing the dynamic demands of care, many nuances are to be considered in the design and implementation of active and assisted living technologies (AAL) for older adult care. This qualitative study, set in southern Spain, is positioned at the crossroads of healthcare challenges, as seen by the different actors involved in the care process and the technological solutions developed in response to these challenges. By investigating the complex landscape of caregiving and by examining the experiences and challenges faced by caregivers, healthcare professionals, and older adults, we aim to guide the development of vision-based AAL technologies that are responsive to the genuine needs of older adults and those requiring care. Methods: A qualitative research methodology was used in the study. In total15 in-depth interviews and five focus groups were conducted with a diverse group of stakeholders involved in the process of care provision and reception. Results: While the results demonstrate that there is a readiness for technological solutions, concerns over privacy and trust highlight the need for a carefully integrated, human-centric approach to technology in caregiving. Conclusion: This research serves as a compass, guiding future discussions on the intersection of aging, technology, and care, with the ultimate goal of transforming caregiving into a collaborative and enriching journey for all stakeholders involved.

Approximate maximum likelihood estimation in cure models using aggregated data, with application to HPV vaccine completion.

Research into vaccine hesitancy is a critical component of the public health enterprise, as rates of communicable diseases preventable by routine childhood immunization have been increasing in recent years. It is therefore important to estimate proportions of "never-vaccinators" in various subgroups of the population in order to successfully target interventions to improve childhood vaccination rates. However, due to privacy issues, it may be difficult to obtain individual patient data (IPD) needed to perform the appropriate time-to-event analyses: state-level immunization information services may only be willing to share aggregated data with researchers. We propose statistical methodology for the analysis of aggregated survival data that can accommodate a cured fraction based on a polynomial approximation of the mixture cure model log-likelihood function relying only on summary statistics. We study the performance of the method through simulation studies and apply it to a real-world data set from a study examining reminder/recall approaches to improve human papillomavirus (HPV) vaccination uptake. The proposed methods may be generalized for use when there is interest in fitting complex likelihood-based models but IPD is unavailable due to data privacy or other concerns.

Disease characteristics influence the privacy calculus to adopt electronic health records: A survey study in Germany.

Background: The electronic health record (EHR) is integral to improving healthcare efficiency and quality. Its successful implementation hinges on patient willingness to use it, particularly in Germany where concerns about data security and privacy significantly influence usage intention. Little is known about how specific characteristics of medical data influence patients' intention to use the EHR. Objective: This study aims to validate the privacy calculus model (PCM) regarding EHRs and to assess how personal and disease characteristics, namely disease-related stigma and disease time course, affect PCM predictions. Methods: An online survey was conducted to empirically validate the PCM for EHR, incorporating a case vignette varying in disease-related stigma (high/low) and time course (acute/chronic), with N = 241 participants, aged 18 years and older residing in Germany with no previous experience with the diseases mentioned in the respective medical reports. Participants were randomized (single-blinded) into four groups in parallel: high stigma and acute time course (n = 74), high stigma and chronic time course (n = 56), low stigma and acute time course (n = 62) and low stigma and chronic time course (n = 49). The data were analyzed using structural equation modeling with partial least squares. Results: The model explains R² = 71.8% of the variance in intention to use. The intention to use is influenced by perceived benefits, data privacy concerns, trust in the provider, and social norms. However, only the disease's time course, not stigma, affects this intention. For acute diseases, perceived benefits and social norms are influential, whereas for chronic diseases, perceived benefits, privacy concerns, and trust in the provider influence intention. Conclusions: The PCM validation for EHRs reveals that personal and disease characteristics shape usage intention in Germany. The need for tailored EHR adoption strategies that address specific needs and concerns of patients with different disease types. Such strategies could lead to a more successful and widespread implementation of EHRs, especially in privacy-conscious contexts.

Factors Influencing Nurses' Intentions to Protect Patient Privacy: A Survey-Based Study Using the Theory of Planned Behavior.

Background and Purpose: Patient privacy and confidentiality are fundamental ethical principles in healthcare. Protecting patient privacy, which is accepted as a patient's right, is one of the responsibilities of nurses. Few studies on patient privacy among nurses have used social cognitive approaches. The purpose of this study is to examine nurses' intentions to protect patient privacy using the theory of planned behavior (TPB). Methods: This is a cross-sectional and correlational design study. The study sample consisted of 202 nurses working in the emergency departments, operating rooms, inpatient wards, and intensive care units of the hospitals. Research data were collected using a face-to-face questionnaire that included TPB components on patient privacy. The proposed research model was tested using structural equation modeling. Results: Attitude (ß = .238, p < .05), subjective norm (ß = .295, p < .05), and moral norm (ß = .337, p < .05) toward patient privacy are positive predictors of intention. The moral norm is the most effective component of intention. Perceived behavioral control is not a significant predictor of intention (ß = .049, p > .05). Implications for Practice: Norms that create a sense of moral obligation in nurses are a significant determinant in increasing the intention to protect patient privacy. Interventions that improve moral norms, attitudes, and subjective norms will increase the intention to protect privacy. Nurse managers should provide nurses with adequate skills, resources, and an appropriate work environment to ensure perceived behavioral control regarding patient privacy.

Privacy in Community Pharmacies in Saudi Arabia: A Cross-Sectional Study.

BACKGROUND: Privacy in healthcare is a fundamental right essential to maintain patient confidentiality and trust. Community pharmacies in Saudi Arabia (SA) play a critical role in the healthcare system by providing accessible services and serving as initial points of contact for medical advice. However, the open nature of these settings poses significant challenges in maintaining patient privacy. METHODS: This cross-sectional study used electronic surveys distributed across various online platforms. The target sample included Saudi adults, with a sample size of 385 participants to achieve 80% statistical power at a 95% confidence interval. The survey comprised demographic questions and sections evaluating perceptions of privacy, the importance of privacy, and personal experiences regarding privacy in community pharmacies. Descriptive statistics and logistic regression models were used for the analysis. RESULTS: A total of 511 responses were obtained. The mean age was 33.5 years, with an almost equal distribution of males (49.71%) and females (50.29%). Most participants held a bachelor's degree or higher (78.67%). Privacy perceptions varied, with only 9.0% strongly agreeing that there was a private space for consultations, while 64.0% felt that the design of community pharmacies did not adequately consider patient privacy, and 86.9% reported that conversations could be overheard. Privacy concerns were notable, with almost one-half of the participants (47.6%) having concerns about privacy and 56.6% doubting the confidentiality of their health information. Moreover, 17.6% reported being asked for unnecessary personal information when buying medication, and 56.2% admitted to avoiding discussing a health problem with the pharmacist due to privacy concerns. Experiences of privacy breaches were reported by 15.7% of respondents. Logistic regression analysis revealed that the availability of private space in the pharmacy and patients feeling that the pharmacy respects their privacy were associated with a lower likelihood of avoiding discussions with pharmacists due to privacy concerns (OR = 0.758, CI = 0.599-0.0957 and OR = 0.715, CI = 0.542-0.945 respectively) Conversely, greater privacy concerns and previous privacy breaches significantly increased the likelihood of avoiding discussions with pharmacists in the community pharmacy (OR = 1.657, CI = 1.317-2.102 and OR = 4.127, CI = 1.886-9.821 respectively). CONCLUSIONS: This study highlights the significant concerns regarding privacy practices in community pharmacies in SA. Thus, there is a need for standards to improve privacy in community pharmacies, such as mandating the need for private consultation areas and enhanced staff training on handling privacy-related issues. Addressing the issue of privacy is crucial for maintaining patient trust, improving healthcare service quality, and ensuring effective patient-pharmacist interactions.

Improved Scheme for Data Aggregation of Distributed Oracle for Intelligent Internet of Things.

Oracle is a data supply mechanism that provides real-world data for blockchain. It serves as a bridge between blockchain and the IoT world, playing a crucial role in solving problems such as data sharing and device management in the IoT field. The main challenge at this stage is determining how to achieve data privacy protection in distributed Oracle machines to safeguard the value hidden in data on the blockchain. In this paper, we propose an improved scheme for distributed Oracle data aggregation based on Paillier encryption algorithm, which achieves end-to-end data privacy protection from devices to users. To address the issue of dishonest distributed Oracle machines running out of funds, we have designed an algorithm called PICA (Paillier-based InChain Aggregation). Based on the aggregation on the Chainlink chain and the Paillier encryption algorithm, random numbers are introduced to avoid the problem of dishonest Oracle machines running out of funds. We use the traffic coverage method to solve the problem of exposed request paths in distributed Oracle machines. Simulation and experimental results show that in small and medium-sized IoT application scenarios with 10,000 data nodes, each additional false request in a single request will result in a delay of about 2 s in data acquisition and can achieve a request response time of 20 s. The proposed method can achieve user data privacy protection.

Enhancing Unmanned Aerial Vehicle Security: A Zero-Knowledge Proof Approach with Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge for Authentication and Location Proof.

UAVs are increasingly being used in various domains, from personal and commercial applications to military operations. Ensuring the security and trustworthiness of UAV communications is crucial, and blockchain technology has been explored as a solution. However, privacy remains a challenge, especially in public blockchains. In this work, we propose a novel approach utilizing zero-knowledge proof techniques, specifically zk-SNARKs, which are non-interactive cryptographic proofs. This approach allows UAVs to prove their authenticity or location without disclosing sensitive information. We generated zk-SNARK proofs using the Zokrates tool on a Raspberry Pi, simulating a drone environment, and analyzed power consumption and CPU utilization. The results are promising, especially in the case of larger drones with higher battery capacities. Ethereum was chosen as the public blockchain platform, with smart contracts developed in Solidity and tested on the Sepolia testnet using Remix IDE. This novel proposed approach paves the way for a new path of research in the UAV area.

Integrating Health and Disability Data Into Academic Information Systems: Workflow Optimization Study.

Background: Integrating health information into university information systems holds significant potential for enhancing student support and well-being. Despite the growing body of research highlighting issues faced by university students, including stress, depression, and disability, little has been done in the informatics field to incorporate health technologies at the institutional level. Objective: This study aims to investigate the current state of health information integration within university systems and provide design recommendations to address existing gaps and opportunities. Methods: We used a user-centered approach to conduct interviews and focus group sessions with stakeholders to gather comprehensive insights and requirements for the system. The methodology involved data collection, analysis, and the development of a suggested workflow. Results: The findings of this study revealed the shortcomings in the current process of handling health and disability data within university information systems. In our results, we discuss some requirements identified for integrating health-related information into student information systems, such as privacy and confidentiality, timely communication, task automation, and disability resources. We propose a workflow that separates the process into 2 distinct components: a health and disability system and measures of quality of life and wellness. The proposed workflow highlights the vital role of academic advisors in facilitating support and enhancing coordination among stakeholders. Conclusions: To streamline the workflow, it is vital to have effective coordination among stakeholders and redesign the university information system. However, implementing the new system will require significant capital and resources. We strongly emphasize the importance of increased standardization and regulation to support the information system requirements for health and disability. Through the adoption of standardized practices and regulations, we can ensure the smooth and effective implementation of the required support system.

De-Identifying GRASCCO - A Pilot Study for the De-Identification of the German Medical Text Project (GeMTeX) Corpus.

INTRODUCTION: The German Medical Text Project (GeMTeX) is one of the largest infrastructure efforts targeting German-language clinical documents. We here introduce the architecture of the de-identification pipeline of GeMTeX. METHODS: This pipeline comprises the export of raw clinical documents from the local hospital information system, the import into the annotation platform INCEpTION, fully automatic pre-tagging with protected health information (PHI) items by the Averbis Health Discovery pipeline, a manual curation step of these pre-annotated data, and, finally, the automatic replacement of PHI items with type-conformant substitutes. This design was implemented in a pilot study involving six annotators and two curators each at the Data Integration Centers of the University Hospitals Leipzig and Erlangen. RESULTS: As a proof of concept, the publicly available Graz Synthetic Text Clinical Corpus (GRASSCO) was enhanced with PHI annotations in an annotation campaign for which reasonable inter-annotator agreement values of Krippendorff's α ≈ 0.97 can be reported. CONCLUSION: These curated 1.4 K PHI annotations are released as open-source data constituting the first publicly available German clinical language text corpus with PHI metadata.

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.