Evaluation of the Opinions of Surgery Patients on Perioperative Privacy.

PURPOSE: To evaluate surgical patients' perceptions of perioperative privacy. DESIGN: A descriptive and cross-sectional study. METHODS: A total of 172 patients who underwent surgical intervention at a state hospital were included. A Patient Information Form and the Perioperative Privacy Scale (PPS) were used to collect data. The data were analyzed using descriptive statistical methods, Mann-Whitney U test, and Kruskal-Wallis H test. FINDINGS: With a mean age of 56.81 ± 1.29 years, 56.4% of the patients were male. Over half of all patients (51.7%) were familiar with the concept of patient privacy, and the vast majority (94.2%) felt that their privacy was protected by the health care staff during their hospital stay. The mean PPS score was 74.38 ± 10.44. A statistically significant difference was found between the patients' marital status, education level, health insurance, attention to privacy by health personnel, and the mean scores of the PPS (P < .05). CONCLUSIONS: The research found that patients who underwent surgery felt that their privacy was well-protected during the perioperative period. To maintain patient privacy during this process, surgical nurses should continue their current practices and emphasize the importance of the subject in in-service training programs.

Psychometric properties of the Chinese version of the Need for Privacy Scale.

Introduction: The need for privacy is a high-order psychological need of human, which is closely related to human mental health problems in the digital age. The Need for Privacy Scale (NFP-S) is a reliable measure of need for privacy. This study tested its psychometric characteristics among Chinese populations. Methods: Firstly, we modified and translated the NFP-S into Chinese version (NFP-SC). Subsequently, we invited 15 participants to complete pre testing of the NFP-SC and determined the final version. Next, we collected questionnaire data from 1130 participants for confirmatory factor analysis to confirm factor structure and validate convergent validity. Results: The results showed that the bifactor Exploratory Structural Equation Modeling (bifactor-ESEM) could better reflect the potential structure of NFP-SC, which included one general factor of need for privacy and three specific factors which were the informational need for privacy, the psychological need for privacy, and the physical need for privacy. Based on the bifactor-ESEM model, the measurement invariance of NFP-SC was demonstrated across gender groups. The general factor and specific factor of NFP-SC showed good reliability with high McDonald's coefficient omega. Convergent validity was tested by verifying the relationship between NFP-SC and four covariates. Conclusions: Our study results showed that NFP-SC exhibited satisfactory psychometric properties in the Chinese context, meaning that it could be applied for future studies on investigating need for privacy in Chinese populations. Future research could build panel data by gathering data from different periods, and supplement the test-retest reliability of NFP-S to improve its application effect.

Automated Psychotherapy in a Spaceflight Environment: Advantages, Drawbacks, and Unknowns.

Various behavioral and mental health issues have been reported by space crews for decades, with the overall number of mental health complications expected to be higher than is publicly known. The broad range of mental health complications encountered in space is expected to grow as people venture deeper into space. Issues with privacy, dual relationships, and delayed communications make rendering effective psychological therapy difficult in a spaceflight environment and nearly impossible in deep space. Automated psychotherapy offers a way to provide psychotherapy to astronauts both in deep space and low Earth orbit. Although automated psychotherapy is growing in popularity on Earth, little is known about its efficacy in space. This viewpoint serves to highlight the knowns and unknowns regarding this treatment modality for future deep space missions, and places an emphasis on the need for further research into the applicability and practicality of automated psychotherapy for the spaceflight environment, especially as it relates to long-duration, deep space missions.

MemberShield: A framework for federated learning with membership privacy.

Federated Learning (FL) allows multiple data owners to build high-quality deep learning models collaboratively, by sharing only model updates and keeping data on their premises. Even though FL offers privacy-by-design, it is vulnerable to membership inference attacks (MIA), where an adversary tries to determine whether a sample was included in the training data. Existing defenses against MIA cannot offer meaningful privacy protection without significantly hampering the model's utility and causing a non-negligible training overhead. In this paper we analyze the underlying causes of the differences in the model behavior for member and non-member samples, which arise from model overfitting and facilitate MIAs. Accordingly, we propose MemberShield, a generalization-based defense method for MIAs that consists of: (i) one-time preprocessing of each client's training data labels that transforms one-hot encoded labels to soft labels and eventually exploits them in local training, and (ii) early stopping the training when the local model's validation accuracy does not improve on that of the global model for a number of epochs. Extensive empirical evaluations on three widely used datasets and four model architectures demonstrate that MemberShield outperforms state-of-the-art defense methods by delivering substantially better practical privacy protection against all forms of MIAs, while better preserving the target model utility. On top of that, our proposal significantly reduces training time and is straightforward to implement, by just tuning a single hyperparameter.

Study of Patients' Privacy during the COVID-19 Pandemic in Iranian Health Care Settings.

Background: Patient's privacy protection is a challenging ethical issue. The complex situation of the COVID-19 pandemic was a probable predictor of breaching confidentiality. This study aimed to assess the viewpoints of COVID-19-confirmed patients, who were hospitalized, and their healthcare providers about the compliance of different aspects of patient's privacy. Methods: This cross-sectional study included 3433 COVID-19-confirmed patients who were hospitalized in Kerman, between 2020 and 2021, and about 1228 related physicians, nurses, and paraclinical staff. Two separate validated researcher-made questionnaires were developed, each including subscales for physical, informational, and spatial privacy, as well as a satisfaction rate of privacy protection. The data were analyzed using SPSS software version 26, with independent samples t test, Mann-Whitney-U, Kruskal Wallis, and Multiple Linear Regression tests at a 95% confidence interval. Results: The mean percentages of the patients' privacy scores in physical, spatial, and informational areas were significantly lower (P<0.001) than the average of the medical staff's scores in all three areas (Difference: 10.27%, 14.83%, and 4.91%, respectively). Physical and spatial privacy scores could be predicted based on the participants' classification, patients or medical staff, and sex. The mean patients' satisfaction score was 9.25% lower than the medical staff's (P<0.001). Moreover, only academic hospitals showed a statistically significant difference between the patient's satisfaction with privacy protection and medical staff's viewpoints (P<0.001). Conclusion: Although this study indicated the benefits of protecting patients' privacy in the healthcare setting, patients' privacy scores and satisfaction were lower than their healthcare providers. The pandemic conditions might have been an obstacle to preserving patients' rights. These findings demonstrated the importance of sensitizing healthcare providers to manage these ethical challenges in a complicated critical state such as the COVID-19 pandemic.

Socio-ethical challenges and opportunities for advancing diversity, equity, and inclusion in digital medicine.

Digitalization in medicine offers a significant opportunity to transform healthcare systems by providing novel digital tools and services to guide personalized prevention, prediction, diagnosis, treatment and disease management. This transformation raises a number of novel socio-ethical considerations for individuals and society as a whole, which need to be appropriately addressed to ensure that digital medical devices (DMDs) are widely adopted and benefit all patients as well as healthcare service providers. In this narrative review, based on a broad literature search in PubMed, Web of Science, Google Scholar, we outline five core socio-ethical considerations in digital medicine that intersect with the notions of equity and digital inclusion: (i) access, use and engagement with DMDs, (ii) inclusiveness in DMD clinical trials, (iii) algorithm fairness, (iv) surveillance and datafication, and (v) data privacy and trust. By integrating literature from multidisciplinary fields, including social, medical, and computer sciences, we shed light on challenges and opportunities related to the development and adoption of DMDs. We begin with an overview of the different types of DMDs, followed by in-depth discussions of five socio-ethical implications associated with their deployment. Concluding our review, we provide evidence-based multilevel recommendations aimed at fostering a more inclusive digital landscape to ensure that the development and integration of DMDs in healthcare mitigate rather than cause, maintain or exacerbate health inequities.

Validation Assessment of Privacy-Preserving Synthetic Electronic Health Record Data: Comparison of Original Versus Synthetic Data on Real-World COVID-19 Vaccine Effectiveness.

PURPOSE: To assess the validity of privacy-preserving synthetic data by comparing results from synthetic versus original EHR data analysis. METHODS: A published retrospective cohort study on real-world effectiveness of COVID-19 vaccines by Maccabi Healthcare Services in Israel was replicated using synthetic data generated from the same source, and the results were compared between synthetic versus original datasets. The endpoints included COVID-19 infection, symptomatic COVID-19 infection and hospitalization due to infection and were also assessed in several demographic and clinical subgroups. In comparing synthetic versus original data estimates, several metrices were utilized: standardized mean differences (SMD), decision agreement, estimate agreement, confidence interval overlap, and Wald test. Synthetic data were generated five times to assess the stability of results. RESULTS: The distribution of demographic and clinical characteristics demonstrated very small difference (< 0.01 SMD). In the comparison of vaccine effectiveness assessed in relative risk reduction between synthetic versus original data, there was a 100% decision agreement, 100% estimate agreement, and a high level of confidence interval overlap (88.7%-99.7%) in all five replicates across all subgroups. Similar findings were achieved in the assessment of vaccine effectiveness against symptomatic COVID-19 Infection. In the comparison of hazard ratios for COVID 19-related hospitalization and odds ratio for symptomatic COVID-19 Infection, the Wald tests suggested no significant difference between respective effect estimates in all five replicates for all patient subgroups but there were disagreements in estimate and decision metrices in some subgroups and replicates. CONCLUSIONS: Overall, comparison of synthetic versus original real-world data demonstrated good validity and reliability. Transparency on the process to generate high fidelity synthetic data and assurances of patient privacy are warranted.

FemTech: empowering reproductive rights or FEM-TRAP for surveillance?

The emergence of FemTech technologies promises to revolutionize women's health and reproductive rights but conceals an insidious trap of surveillance and control in the hands of private and state actors. This article examines the extent to which FemTech technologies, under the guise of empowerment, enable private actors to play a leading role in managing reproductive rights, replacing largely inactive States in this crucial function. The analysis shows how private FemTech companies are becoming critical players in implementing and defending these rights, often in response to the inaction or inadequacies of States. The article approaches the FemTech phenomenon from several angles, including the promises of empowerment, concerns about surveillance and control, and the ambivalent roles of private actors as implementers and defenders of reproductive rights. This structure makes it possible to offer a critical analysis of the legal, societal, and ethical implications of FemTech, highlighting the tensions between the promises of empowerment and the risks of surveillance and control.

Private information leakage from single-cell count matrices.

The increase in publicly available human single-cell datasets, encompassing millions of cells from many donors, has significantly enhanced our understanding of complex biological processes. However, the accessibility of these datasets raises significant privacy concerns. Due to the inherent noise in single-cell measurements and the scarcity of population-scale single-cell datasets, recent private information quantification studies have focused on bulk gene expression data sharing. To address this gap, we demonstrate that individuals in single-cell gene expression datasets are vulnerable to linking attacks, where attackers can infer their sensitive phenotypic information using publicly available tissue or cell-type-specific expression quantitative trait loci (eQTLs) information. We further develop a method for genotype prediction and genotype-phenotype linking that remains effective without relying on eQTL information. We show that variants from one study can be exploited to uncover private information about individuals in another study.

A secure healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain.

Enhancing data privacy security in medical data sharing is crucial for the informatization development in the healthcare sector. This paper proposes a healthcare data sharing scheme based on two-dimensional chaotic mapping and blockchain (2DCM-DS). Specifically, a new two-dimensional chaotic mapping is proposed, which demonstrates superior chaotic performance. Then, by incorporating biometric audio information as an identity credential and integrating it with the proposed two-dimensional chaotic mapping, we design a data encryption method that establishes a strongly coupled and bi-directionally verifiable data ownership relationship in healthcare data sharing. Finally, we employ blockchain as the underlying network and design corresponding smart contracts to support 2DCM-DS. This approach addresses potential issues of unauthorized access, malicious tampering, and single points of failure in centralized data sharing. Experimental results demonstrate that 2DCM-DS effectively protects data security under the specified attack models. The results validate the security and efficiency of the 2DCM-DS, proving its application potential in healthcare insurance data sharing scenarios.

The Automatic Context Measurement Tool (ACMT) to Compile Participant-Specific Built and Social Environment Measures for Health Research: Development and Usability Study.

BACKGROUND: The environment shapes health behaviors and outcomes. Studies exploring this influence have been limited to research groups with the geographic information systems expertise required to develop built and social environment measures (eg, groups that include a researcher with geographic information system expertise). OBJECTIVE: The goal of this study was to develop an open-source, user-friendly, and privacy-preserving tool for conveniently linking built, social, and natural environmental variables to study participant addresses. METHODS: We built the automatic context measurement tool (ACMT). The ACMT comprises two components: (1) a geocoder, which identifies a latitude and longitude given an address (currently limited to the United States), and (2) a context measure assembler, which computes measures from publicly available data sources linked to a latitude and longitude. ACMT users access both of these components using an RStudio/RShiny-based web interface that is hosted within a Docker container, which runs on a local computer and keeps user data stored in local to protect sensitive data. We illustrate ACMT with 2 use cases: one comparing population density patterns within several major US cities, and one identifying correlates of cannabis licensure status in Washington State. RESULTS: In the population density analysis, we created a line plot showing the population density (x-axis) in relation to distance from the center of the city (y-axis, using city hall location as a proxy) for Seattle, Los Angeles, Chicago, New York City, Nashville, Houston, and Boston with the distances being 1000, 2000, 3000, 4000, and 5000 m. We found the population density tended to decrease as distance from city hall increased except for Nashville and Houston, 2 cities that are notably more sprawling than the others. New York City had a significantly higher population density than the others. We also observed that Los Angeles and Seattle had similarly low population densities within up to 2500 m of City Hall. In the cannabis licensure status analysis, we gathered neighborhood measures such as age, sex, commute time, and education. We found the strongest predictive characteristic of cannabis license approval to be the count of female children aged 5 to 9 years and the proportion of females aged 62 to 64 years who were not in the labor force. However, after accounting for Bonferroni error correction, none of the measures were significantly associated with cannabis retail license approval status. CONCLUSIONS: The ACMT can be used to compile environmental measures to study the influence of environmental context on population health. The portable and flexible nature of ACMT makes it optimal for neighborhood study research seeking to attribute environmental data to specific locations within the United States.

Challenges and applications in generative AI for clinical tabular data in physiology.

Recent advancements in generative approaches in AI have opened up the prospect of synthetic tabular clinical data generation. From filling in missing values in real-world data, these approaches have now advanced to creating complex multi-tables. This review explores the development of techniques capable of synthesizing patient data and modeling multiple tables. We highlight the challenges and opportunities of these methods for analyzing patient data in physiology. Additionally, it discusses the challenges and potential of these approaches in improving clinical research, personalized medicine, and healthcare policy. The integration of these generative models into physiological settings may represent both a theoretical advancement and a practical tool that has the potential to improve mechanistic understanding and patient care. By providing a reliable source of synthetic data, these models can also help mitigate privacy concerns and facilitate large-scale data sharing.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Decoding privacy concerns: the role of perceived risk and benefits in personal health data disclosure.

BACKGROUND: Personal health data is crucial for effective medical care, personalized treatment, and health monitoring. It enables accurate diagnosis, efficient treatment plans, and informed healthcare decisions. Personal health data should be protected to ensure patient privacy, prevent misuse or unauthorized access, and maintain trust in healthcare systems, thereby safeguarding individuals' sensitive information from potential harm or exploitation. Therefore, this study aimed to investigate whether perceived risk and perceived benefits have mediating roles in the relationships among individuals' personal health information disclosure behaviour, perceived control, and privacy concerns. METHOD: The population of the study consisted of individuals living in the provinces of Izmir, Konya and Adana. The sample of the study consisted of individuals who were reached through a convenience sampling method. The scales for privacy concerns, perceived control, perceived risk, perceived benefits and information disclosure behaviour were used in the study. Cronbach's alpha and the AVE were calculated, and a confirmatory factor analysis was performed. A path analysis was performed using the structural equation model to test the hypotheses. RESULTS: The analysis revealed a significant negative relationship between individuals' personal health data disclosure behaviour and their privacy concerns. However, perceived risk and perceived benefit did not mediate this relationship. Additionally, a significant positive relationship was found between individuals' behaviour of disclosing their perceived control and personal health data, with perceived risk and benefits playing a mediating role in this relationship. CONCLUSION: The study concluded that as individuals' concerns about sharing personal health data increase, they are less likely to share these data. It was also found that perceived risk and perceived benefit mediate this relationship. Additionally, higher perceived risk intensifies privacy concerns, further discouraging data sharing, while perceived benefits can mitigate these concerns, promoting greater willingness to disclose health information.

Federated learning: Overview, strategies, applications, tools and future directions.

Federated learning (FL) is a distributed machine learning process, which allows multiple nodes to work together to train a shared model without exchanging raw data. It offers several key advantages, such as data privacy, security, efficiency, and scalability, by keeping data local and only exchanging model updates through the communication network. This review paper provides a comprehensive overview of federated learning, including its principles, strategies, applications, and tools along with opportunities, challenges, and future research directions. The findings of this paper emphasize that federated learning strategies can significantly help overcome privacy and confidentiality concerns, particularly for high-risk applications.

Ethereum blockchain for electronic health records: securing and streamlining patient management.

Electronic health records (EHRs) are increasingly replacing traditional paper-based medical records due to their speed, security, and ability to eliminate redundant data. However, challenges such as EHR interoperability and privacy concerns remain unresolved. Blockchain, a distributed ledger technology comprising connected, encrypted data blocks, presents a promising solution. This study explores how blockchain technology can revolutionize hospital EHR management. Our proposed solution securely transfers medical records between patients and doctors using the InterPlanetary File System (IPFS) and the Ethereum platform. Utilizing smart contracts automates data transfers, ensuring patient anonymity and reducing computational complexity while securely storing patient data on the network. Patient records are stored locally on the Ganache server, with the front end managed using HTML, CSS, ReactJS, and JavaScript, and the backend developed in Solidity. Blockchain technologies combined with Role- Based access control instead of attribute -based access control. The system's throughput increases linearly with the number of users and requests, enhancing the framework's efficiency and scalability. The minimum recorded latency is 14 ms.

[The place of privacy in the caregiver-caregiver relationship: a descriptive exploratory study]. / La place du respect de l'intimité dans la relation soignant-soignée : une étude exploratoire descriptive.

During care, privacy is subject to physical or moral disrespect. This crucial right of the patient is increasingly neglected in the care-giver-patient relationship. However, it is observed that this is linked to soft skills, and that the majority of healthcare professionals have only one objective, which is the mastery of technical skills. The aim of the present study is to explore and describe in depth the place of respect for privacy in the care-giver-patient relationship in the maternity department of a provincial hospital in Casablanca-Settat region, in Morocco.

Privacy-preserving distributed frequency control for AC microgrids with constrained communication.

Distributed control of AC microgrids is one of the most popular methods in the islanded operation mode. Whereas, most of the existing studies either do not consider the potential threat of privacy security or rely on the assumption of ideal communication networks. To this end, this paper presents a novel privacy-preserving distributed secondary frequency control strategy for the privacy protection problem of an islanded AC microgrid with constrained communication. The key contributions of this paper are threefold. (1) Different from the existing privacy-preserving approaches used in AC microgrids, a time-varying function is introduced to mask interactive information such that the frequency cannot be reconstructed by malicious attackers. (2) An event-triggered communication scheme is employed to cope with the constrained communication environment. (3) A privacy-preserving distributed event-triggered control strategy with communication delay is developed such that the frequency restoration and active power sharing of the microgrid are guaranteed. Moreover, the maximum communication delay that the proposed control can withstand is analyzed. Simulation results show the properties of the privacy preservation, the decrease of communication load, and the bounded communication delay allowed in the proposed control strategy.