Unlocking Public Health Data: Navigating New Legal Guardrails and Emerging AI Challenges.

Here, we analyze the public health implications of recent legal developments - including privacy legislation, intergovernmental data exchange, and artificial intelligence governance - with a view toward the future of public health informatics and the potential of diverse data to inform public health actions and drive population health outcomes.

Decentralising the Self - Ethical Considerations in Utilizing Decentralised Web Technology for Direct Brain Interfaces.

The rapidly advancing field of brain-computer (BCI) and brain-to-brain interfaces (BBI) is stimulating interest across various sectors including medicine, entertainment, research, and military. The developers of large-scale brain-computer networks, sometimes dubbed 'Mindplexes' or 'Cloudminds', aim to enhance cognitive functions by distributing them across expansive networks. A key technical challenge is the efficient transmission and storage of information. One proposed solution is employing blockchain technology over Web 3.0 to create decentralised cognitive entities. This paper explores the potential of a decentralised web for coordinating large brain-computer constellations, and its associated benefits, focusing in particular on the conceptual and ethical challenges this innovation may pose pertaining to (1) Identity, (2) Sovereignty (encompassing Autonomy, Authenticity, and Ownership), (3) Responsibility and Accountability, and (4) Privacy, Safety, and Security. We suggest that while a decentralised web can address some concerns and mitigate certain risks, underlying ethical issues persist. Fundamental questions about entity definition within these networks, the distinctions between individuals and collectives, and responsibility distribution within and between networks, demand further exploration.

Self-learning activation functions to increase accuracy of privacy-preserving Convolutional Neural Networks with homomorphic encryption.

The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional Neural Networks with Homomorphic Encryption (CNN-HE) by Self-Learning Activation Functions (SLAF). SLAFs are polynomials with trainable coefficients updated during training, together with synaptic weights, for each polynomial independently to learn task-specific and CNN-specific features. We theoretically prove its feasibility to approximate any continuous activation function to the desired error as a function of the SLAF degree. Two CNN-HE models are proposed: CNN-HE-SLAF and CNN-HE-SLAF-R. In the first model, all activation functions are replaced by SLAFs, and CNN is trained to find weights and coefficients. In the second one, CNN is trained with the original activation, then weights are fixed, activation is substituted by SLAF, and CNN is shortly re-trained to adapt SLAF coefficients. We show that such self-learning can achieve the same accuracy 99.38% as a non-polynomial ReLU over non-homomorphic CNNs and lead to an increase in accuracy (99.21%) and higher performance (6.26 times faster) than the state-of-the-art CNN-HE CryptoNets on the MNIST optical character recognition benchmark dataset.

Ethical considerations during Mpox Outbreak: a scoping review.

BACKGROUND: Historically, epidemics have been accompanied by the concurrent emergence of stigma, prejudice, and xenophobia. This scoping review aimed to describe and map published research targeting ethical values concerning monkeypox (mpox). In addition, it aimed to understand the research gaps related to mpox associated stigma. METHODS: We comprehensively searched databases (PubMed Central, PubMed Medline, Scopus, Web of Science, Ovid, and Google Scholar) to identify published literature concerning mpox ethical issues and stigma from May 6, 2022, to February 15, 2023. The key search terms used were "monkeypox", "ethics", "morals", "social stigma", "privacy", "confidentiality", "secrecy", "privilege", "egoism", and "metaethics". This scoping review followed the framework proposed by Arksey and O'Malley in 2005 and was further improved by the recommendations of Levac et al. in 2010. RESULTS: The search strategies employed in the scoping review yielded a total of 454 articles. We analyzed the sources, types, and topics of the retrieved articles/studies. The authors were able to identify 32 studies that met inclusion criteria. Six of the 32 included studies were primary research. The study revealed that the ongoing mpox outbreak is contending with a notable surge in misinformation and societal stigma. It highlights the adverse impacts of stigma and ethical concerns associated with mpox, which can negatively affect people with the disease. CONCLUSION: The study's findings underscore the imperative need to enhance public awareness; involve civil society; and promote collaboration among policymakers, medical communities, and social media platforms. These collective endeavors are crucial for mitigating stigma, averting human-to-human transmission, tackling racism, and dispelling misconceptions associated with the outbreak.

Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework.

The timely identification of autism spectrum disorder (ASD) in children is imperative to prevent potential challenges as they grow. When sharing data related to autism for an accurate diagnosis, safeguarding its security and privacy is a paramount concern to fend off unauthorized access, modification, or theft during transmission. Researchers have devised diverse security and privacy models or frameworks, most of which often leverage proprietary algorithms or adapt existing ones to address data leakage. However, conventional anonymization methods, although effective in the sanitization process, proved inadequate for the restoration process. Furthermore, despite numerous scholarly contributions aimed at refining the restoration process, the accuracy of restoration remains notably deficient. Based on the problems identified above, this paper presents a novel approach to data restoration for sanitized sensitive autism datasets with improved performance. In the prior study, we constructed an optimal key for the sanitization process utilizing the proposed Enhanced Combined PSO-GWO framework. This key was implemented to conceal sensitive autism data in the database, thus avoiding information leakage. In this research, the same key was employed during the data restoration process to enhance the accuracy of the original data recovery. Therefore, the study enhanced the restoration process for ASD data's security and privacy by utilizing an optimal key produced via the Enhanced Combined PSO-GWO framework. When compared to existing meta-heuristic algorithms, the simulation results from the autism data restoration experiments demonstrated highly competitive accuracies with 99.90%, 99.60%, 99.50%, 99.25%, and 99.70%, respectively. Among the four types of datasets used, this method outperforms other existing methods on the 30-month autism children dataset, mostly.

Reducing privacy risks of China's healthcare big data through the policy framework.

While healthcare big data brings great opportunities and convenience to the healthcare industry, it also inevitably raises the issue of privacy leakage. Nowadays, the whole world is facing the security threat of healthcare big data, for which a sound policy framework can help reduce privacy risks of healthcare big data. In recent years, the Chinese government and industry self-regulatory organizations have issued a series of policy documents to reduce privacy risks of healthcare big data. However, China's policy framework suffers from the drawbacks of the mismatched operational model, the inappropriate operational method, and the poorly actionable operational content. Based on the experiences of the European Union, Australia, the United States, and other extra-territorial regions, strategies are proposed for China to amend the operational model of the policy framework, improve the operational method of the policy framework, and enhance the operability of the operational content of the policy framework. This study enriches the research on China's policy framework to reduce privacy risks of healthcare big data and provides some inspiration for other countries.

Collaborative learning from distributed data with differentially private synthetic data.

BACKGROUND: Consider a setting where multiple parties holding sensitive data aim to collaboratively learn population level statistics, but pooling the sensitive data sets is not possible due to privacy concerns and parties are unable to engage in centrally coordinated joint computation. We study the feasibility of combining privacy preserving synthetic data sets in place of the original data for collaborative learning on real-world health data from the UK Biobank. METHODS: We perform an empirical evaluation based on an existing prospective cohort study from the literature. Multiple parties were simulated by splitting the UK Biobank cohort along assessment centers, for which we generate synthetic data using differentially private generative modelling techniques. We then apply the original study's Poisson regression analysis on the combined synthetic data sets and evaluate the effects of 1) the size of local data set, 2) the number of participating parties, and 3) local shifts in distributions, on the obtained likelihood scores. RESULTS: We discover that parties engaging in the collaborative learning via shared synthetic data obtain more accurate estimates of the regression parameters compared to using only their local data. This finding extends to the difficult case of small heterogeneous data sets. Furthermore, the more parties participate, the larger and more consistent the improvements become up to a certain limit. Finally, we find that data sharing can especially help parties whose data contain underrepresented groups to perform better-adjusted analysis for said groups. CONCLUSIONS: Based on our results we conclude that sharing of synthetic data is a viable method for enabling learning from sensitive data without violating privacy constraints even if individual data sets are small or do not represent the overall population well. Lack of access to distributed sensitive data is often a bottleneck in biomedical research, which our study shows can be alleviated with privacy-preserving collaborative learning methods.

End-to-end pseudonymization of fine-tuned clinical BERT models : Privacy preservation with maintained data utility.

Many state-of-the-art results in natural language processing (NLP) rely on large pre-trained language models (PLMs). These models consist of large amounts of parameters that are tuned using vast amounts of training data. These factors cause the models to memorize parts of their training data, making them vulnerable to various privacy attacks. This is cause for concern, especially when these models are applied in the clinical domain, where data are very sensitive. Training data pseudonymization is a privacy-preserving technique that aims to mitigate these problems. This technique automatically identifies and replaces sensitive entities with realistic but non-sensitive surrogates. Pseudonymization has yielded promising results in previous studies. However, no previous study has applied pseudonymization to both the pre-training data of PLMs and the fine-tuning data used to solve clinical NLP tasks. This study evaluates the effects on the predictive performance of end-to-end pseudonymization of Swedish clinical BERT models fine-tuned for five clinical NLP tasks. A large number of statistical tests are performed, revealing minimal harm to performance when using pseudonymized fine-tuning data. The results also find no deterioration from end-to-end pseudonymization of pre-training and fine-tuning data. These results demonstrate that pseudonymizing training data to reduce privacy risks can be done without harming data utility for training PLMs.

Privacy-preserving blockchain-based federated learning for brain tumor segmentation.

Improved data sharing between healthcare providers can lead to a higher probability of accurate diagnosis, more effective treatments, and enhanced capabilities of healthcare organizations. One critical area of focus is brain tumor segmentation, a complex task due to the heterogeneous appearance, irregular shape, and variable location of tumors. Accurate segmentation is essential for proper diagnosis and effective treatment planning, yet current techniques often fall short due to these complexities. However, the sensitive nature of health data often prohibits its sharing. Moreover, the healthcare industry faces significant issues, including preserving the privacy of the model and instilling trust in the model. This paper proposes a framework to address these privacy and trust issues by introducing a mechanism for training the global model using federated learning and sharing the encrypted learned parameters via a permissioned blockchain. The blockchain-federated learning algorithm we designed aggregates gradients in the permissioned blockchain to decentralize the global model, while the introduced masking approach retains the privacy of the model parameters. Unlike traditional raw data sharing, this approach enables hospitals or medical research centers to contribute to a globally learned model, thereby enhancing the performance of the central model for all participating medical entities. As a result, the global model can learn about several specific diseases and benefit each contributor with new disease diagnosis tasks, leading to improved treatment options. The proposed algorithm ensures the quality of model data when aggregating the local model, using an asynchronous federated learning procedure to evaluate the shared model's quality. The experimental results demonstrate the efficacy of the proposed scheme for the critical and challenging task of brain tumor segmentation. Specifically, our method achieved a 1.99% improvement in Dice similarity coefficient for enhancing tumors and a 19.08% reduction in Hausdorff distance for whole tumors compared to the baseline methods, highlighting the significant advancement in segmentation performance and reliability.

Improving home-like environments in long-term care units: an exploratory mixed-method study.

Although the number of older adults requiring care is rapidly increasing, nursing homes have long faced issues such as the absence of a home-like environment. This exploratory mixed-method study investigated how residents (n = 15) in a long-term care unit in South Korea perceive home-like features and privacy in their living spaces. The results indicated that most participants were satisfied with the homeliness and privacy of their environment, but some were unhappy with the level of privacy. Most participants had low scores on the Geriatric Depression Scale and the Pittsburgh Sleep Quality Index, indicating low levels of depression and sleep disorders. Sleep quality was affected by factors such as sensory environment, staff visits, and room temperature. Although participants appreciated social support and private rooms, they expressed a desire for larger rooms. Overall, this study provides preliminary insights into older adults' views on their living spaces in long-term care with implications for improving their quality of life.

The Ninth Amendment: An Underutilized Protection for Reproductive Choice.

Concern about individual rights and the desire to protect them has been part of our nation since its founding, and continues to be so today. The Ninth Amendment was created to assuage the Framers' concerns that enumerating some rights in the Bill of Rights would leave unenumerated rights unrecognized and unprotected, affirming that those rights are not disparaged or denied by their lack of textual support. The Ninth Amendment has appeared infrequently in our jurisprudence, and Courts initially construed it rather narrowly. But starting in the 1960s, the Ninth Amendment emerged as a powerful tool not just for recognizing unanticipated rights, but for protecting or expanding even enumerated rights. The right to privacy--encompassing the right to contraception and abortion--the right to preserve the integrity of your family, the right to vote, the right to own a firearm as an individual--all these rights have been asserted under and found to be supported by the Ninth Amendment. In its Dobbs v. Jackson Women's Health decision overturning Roe, the Supreme Court found that there is no right to abortion because it is not in the Constitution. But the potential of the Ninth Amendment is such that reproductive choice need not be mentioned in the Constitution to be protected. Reproductive choice may rightfully be considered as part of a right to privacy, an unenumerated right that nevertheless has abundant precedent behind it. The Ninth Amendment, and its counterparts found in many state constitutions, has the power to protect not just reproductive choice, but all of our fundamental rights.

Without Due Process of Law: The Dobbs Decision and Its Cataclysmic Impact on the Substantive Due Process and Privacy Rights of Ohio Women.

Since the overturning of prior abortion precedents in Dobbs v. Jackson Women's Health Organization, there has been a question on the minds of many women in this country: how will this decision affect me and my rights? As we have seen in the aftermath of Dobbs, many states have pushed for stringent anti-abortion measures seeking to undermine the foundation on which women's reproductive freedom had been grounded on for decades. This includes right here in Ohio, where Republican lawmakers have advocated on numerous occasions for implementing laws seeking to limit abortion rights, including a 6-week abortion ban advocated for and passed by the Ohio Republican legislature and signed into law by Ohio Governor Mike DeWine. Despite this particular ban being successfully challenged and stayed, significant problems persist regarding due process rights for women in Ohio, particularly in the aftermath of Justice Thomas's concurrence in Dobbs advising the Court to revisit prior precedents, such as Griswold v. Connecticut providing for the right to contraception. If the Court were to revisit and strike down Griswold, it would further undermine privacy and due process rights that have been granted to women across this country, including here in Ohio, for decades. Justice Thomas's concurrence, while merely dicta, encapsulates a Court that has become increasingly hostile to treasured fundamental rights for women, a hostility mirrored in numerous Republican legislatures, including right here in Ohio.

The Dead Unborn, Postmortem Privacy Cases, and Abortion Rights.

The privacy of the dead is an interesting area of concern for bioethicists. There is a legal doctrine that the dead can't have privacy rights, but also a body of contrary law ascribing privacy rights to the deceased and kin in relation to the deceased. As women's abortion privacy is under assault by American courts and legislatures, the implications of ascribing privacy rights to embryos and fetuses is more important than ever. Caution is called for in this domain.

Digital abuse among dating partners: perspectives and experiences of adolescents from two Brazilian capitals.

The conceptions, values, and experiences of students from public and private high schools in two Brazilian state capitals, Vitória-ES and Campo Grande-MS, were analyzed regarding digital control and monitoring between intimate partners and the unauthorized exposure of intimate material on the Internet. Data from eight focus groups with 77 adolescents were submitted to thematic analysis, complemented by a questionnaire answered by a sample of 530 students. Most students affirmed that they do not tolerate the control/monitoring and unauthorized exposure of intimate materials but recognized that such activity is routine. They point out jealousy, insecurity, and "curiosity" as their main reasons. They detail the various dynamics of unauthorized exposure of intimate material and see it as a severe invasion of privacy and a breach of trust between partners. Their accounts suggest that such practices are gender violence. They also reveal that each platform has its cultural appropriation and that platforms used by the family, such as Facebook, cause more significant damage to the victim's reputation.

When Parents Request Nondisclosure: Rights of Adolescents to Access Their Health Information and Implications of the 21st Century Cures Act Final Rule.

AbstractDespite broad ethical consensus supporting developmentally appropriate disclosure of health information to older children and adolescents, cases in which parents and caregivers request nondisclosure continue to pose moral dilemmas for clinicians. State laws vary considerably regarding adolescents' rights to autonomy, privacy, and confidentiality, with many states not specifically addressing adolescents' right to their own healthcare information. The requirements of the 21st Century Cures Act have raised important ethical concerns for pediatricians and adolescent healthcare professionals regarding the protection of adolescent privacy and confidentiality, given requirements that chart notes and results be made readily available to patients via electronic portals. Less addressed have been the implications of the act for adolescents' access to their health information, since many healthcare systems' electronic portals are available to patients beginning at age 12, sometimes requiring that the patients themselves authorize their parents' access to the same information. In this article, we present a challenging case of protracted disagreement about an adolescent's right to honest information regarding his devastating prognosis. We then review the legal framework governing adolescents' rights to their own healthcare information, the limitations of ethics consultation to resolve such disputes, and the potential for the Cures Act's impact on electronic medical record systems to provide one form of resolution. We conclude that although parents in cases like the one presented here have the legal right to consent to medical treatment on their children's behalf, they do not have a corresponding right to direct the withholding of medical information from the patient.

Between Technological Utopia and Dystopia: Online Expression of Compulsory Use of Surveillance Technology.

This study investigated people's ethical concerns of surveillance technology. By adopting the spectrum of technological utopian and dystopian narratives, how people perceive a society constructed through the compulsory use of surveillance technology was explored. This study empirically examined the anonymous online expression of attitudes toward the society-wide, compulsory adoption of a contact tracing app that affected almost every aspect of all people's everyday lives at a societal level. By applying the structural topic modeling approach to analyze comments on four Hong Kong anonymous discussion forums, topics concerning the technological utopian, dystopian, and pragmatic views on the surveillance app were discovered. The findings showed that people with a technological utopian view on this app believed that the implementation of compulsory app use can facilitate social good and maintain social order. In contrast, individuals who had a technological dystopian view expressed privacy concerns and distrust of this surveillance technology. Techno-pragmatists took a balanced approach and evaluated its implementation practically.

Changing US Support for Public Health Data Use Through Pandemic and Political Turmoil.

Policy Points This study examines the impact of several world-changing events in 2020, such as the pandemic and widespread racism protests, on the US population's comfort with the use of identifiable data for public health. Before the 2020 election, there was no significant difference between Democrats and Republicans. However, African Americans exhibited a decrease in comfort that was different from other subgroups. Our findings suggest that the public remained supportive of public health data activities through the pandemic and the turmoil of 2020 election cycle relative to other data use. However, support among African Americans for public health data use experienced a unique decline compared to other demographic groups. CONTEXT: Recent legislative privacy efforts have not included special provisions for public health data use. Although past studies documented support for public health data use, several global events in 2020 have raised awareness and concern about privacy and data use. This study aims to understand whether the events of 2020 affected US privacy preferences on secondary uses of identifiable data, focusing on public health and research uses. METHODS: We deployed two online surveys-in February and November 2020-on data privacy attitudes and preferences using a choice-based-conjoint analysis. Participants received different data-use scenario pairs-varied by the type of data, user, and purpose-and selected scenarios based on their comfort. A hierarchical Bayes regression model simulated population preferences. FINDINGS: There were 1,373 responses. There was no statistically significant difference in the population's data preferences between February and November, each showing the highest comfort with population health and research data activities and the lowest with profit-driven activities. Most subgroups' data preferences were comparable with the population's preferences, except African Americans who showed significant decreases in comfort with population health and research. CONCLUSIONS: Despite world-changing events, including a pandemic, we found bipartisan public support for using identifiable data for public health and research. The decreasing support among African Americans could relate to the increased awareness of systemic racism, its harms, and persistent disparities. The US population's preferences support including legal provisions that permit public health and research data use in US laws, which are currently lacking specific public health use permissions.

Encrypted data-sharing for preserving privacy in wastewater-based epidemiology.

The rapidly expanding use of wastewater for public health surveillance requires new strategies to protect privacy rights, while data are collected at increasingly discrete geospatial scales, i.e., city, neighborhood, campus, and building-level. Data collected at high geospatial resolution can inform on labile, short-lived biomarkers, thereby making wastewater-derived data both more actionable and more likely to cause privacy concerns and stigmatization of subpopulations. Additionally, data sharing restrictions among neighboring cities and communities can complicate efforts to balance public health protections with citizens' privacy. Here, we have created an encrypted framework that facilitates the sharing of sensitive population health data among entities that lack trust for one another (e.g., between adjacent municipalities with different governance of health monitoring and data sharing). We demonstrate the utility of this approach with two real-world cases. Our results show the feasibility of sharing encrypted data between two municipalities and a laboratory, while performing secure private computations for wastewater-based epidemiology (WBE) with high precision, fast speeds, and low data costs. This framework is amenable to other computations used by WBE researchers including population normalized mass loads, fecal indicator normalizations, and quality control measures. The Centers for Disease Control and Prevention's National Wastewater Surveillance System shows ∼8 % of the records attributed to collection before the wastewater treatment plant, illustrating an opportunity to further expand currently limited community-level sampling and public health surveillance through security and responsible data-sharing as outlined here.