[Privacy and health data management.] / Privacy e gestione del dato sanitario.

The effective use of data in healthcare, and the use of information to support decision-making processes is a key issue. Experiencing the Covid-19 pandemic drove to important developments in a relatively short time. In this context, Cittadinanzattiva, which has been dealing with citizens' rights in the health field for years, is really interested in exploring the boundaries between citizens' right to privacy and the promotion of health as a fundamental human right. New strategies to protect the individual and his dignity should be identified, without hindering the use of data to support health policy. The relationship between health and privacy is a pivotal issue because it involves two of the fundamental rights that are most exposed to the evolution of technology and innovation.

The utility of Zip4 codes in spatial epidemiological analysis.

There are many public health situations within the United States that require fine geographical scale data to effectively inform response and intervention strategies. However, a condition for accessing and analyzing such data, especially when multiple institutions are involved, is being able to preserve a degree of spatial privacy and confidentiality. Hospitals and state health departments, who are generally the custodians of these fine-scale health data, are sometimes understandably hesitant to collaborate with each other due to these concerns. This paper looks at the utility and pitfalls of using Zip4 codes, a data layer often included as it is believed to be "safe", as a source for sharing fine-scale spatial health data that enables privacy preservation while maintaining a suitable precision for spatial analysis. While the Zip4 is widely supplied, researchers seldom utilize it. Nor is its spatial characteristics known by data guardians. To address this gap, we use the context of a near-real time spatial response to an emerging health threat to show how the Zip4 aggregation preserves an underlying spatial structure making it potentially suitable dataset for analysis. Our results suggest that based on the density of urbanization, Zip4 centroids are within 150 meters of the real location almost 99% of the time. Spatial analysis experiments performed on these Zip4 data suggest a far more insightful geographic output than if using more commonly used aggregation units such as street lines and census block groups. However, this improvement in analytical output comes at a spatial privy cost as Zip4 centroids have a higher potential of compromising spatial anonymity with 73% of addresses having a spatial k anonymity value less than 5 when compared to other aggregations. We conclude that while offers an exciting opportunity to share data between organizations, researchers and analysts need to be made aware of the potential for serious confidentiality violations.

An LBS and agent-based simulator for Covid-19 research.

The mobility data of citizens provide important information on the epidemic spread including Covid-19. However, the privacy versus security dilemma hinders the utilization of such data. This paper proposed a method to generate pseudo mobility data on a per-agent basis, utilizing the actual geographical environment data provided by LBS to generate the agent-specific mobility trajectories and export them as GPS-like data. Demographic characteristics such as behavior patterns, gender, age, vaccination, and mask-wearing status are also assigned to the agents. A web-based data generator was implemented, enabling users to make detailed settings to meet different research needs. The simulated data indicated the usability of the proposed methods.

Some examples of privacy-preserving sharing of COVID-19 pandemic data with statistical utility evaluation.

BACKGROUND: A considerable amount of various types of data have been collected during the COVID-19 pandemic, the analysis and understanding of which have been indispensable for curbing the spread of the disease. As the pandemic moves to an endemic state, the data collected during the pandemic will continue to be rich sources for further studying and understanding the impacts of the pandemic on various aspects of our society. On the other hand, naïve release and sharing of the information can be associated with serious privacy concerns. METHODS: We use three common but distinct data types collected during the pandemic (case surveillance tabular data, case location data, and contact tracing networks) to illustrate the publication and sharing of granular information and individual-level pandemic data in a privacy-preserving manner. We leverage and build upon the concept of differential privacy to generate and release privacy-preserving data for each data type. We investigate the inferential utility of privacy-preserving information through simulation studies at different levels of privacy guarantees and demonstrate the approaches in real-life data. All the approaches employed in the study are straightforward to apply. RESULTS: The empirical studies in all three data cases suggest that privacy-preserving results based on the differentially privately sanitized data can be similar to the original results at a reasonably small privacy loss ([Formula: see text]). Statistical inferences based on sanitized data using the multiple synthesis technique also appear valid, with nominal coverage of 95% confidence intervals when there is no noticeable bias in point estimation. When [Formula: see text] and the sample size is not large enough, some privacy-preserving results are subject to bias, partially due to the bounding applied to sanitized data as a post-processing step to satisfy practical data constraints. CONCLUSIONS: Our study generates statistical evidence on the practical feasibility of sharing pandemic data with privacy guarantees and on how to balance the statistical utility of released information during this process.

Privacy and Security Risk Factors Related to Telehealth Services - A Systematic Review.

The objective of the study is to identify challenges and associated factors for privacy and security related to telehealth visits during the COVID-19 pandemic. The systematic search strategy used the databases of PubMed, ScienceDirect, ProQuest, Embase, CINAHL, and COCHRANE, with the search terms of telehealth/telemedicine, privacy, security, and confidentiality. Reviews included peer-reviewed empirical studies conducted from January 2020 to February 2022. Studies conducted outside of the US, non-empirical, and non-telehealth related were excluded. Eighteen studies were included in the final analysis. Three risk factors associated with privacy and security in telehealth practice included: environmental factors (lack of private space for vulnerable populations, difficulty sharing sensitive health information remotely), technology factors (data security issues, limited access to the internet, and technology), and operational factors (reimbursement, payer denials, technology accessibility, training, and education). Findings from this study can assist governments, policymakers, and healthcare organizations in developing best practices in telehealth privacy and security strategies.

CoVnita, an end-to-end privacy-preserving framework for SARS-CoV-2 classification.

Classification of viral strains is essential in monitoring and managing the COVID-19 pandemic, but patient privacy and data security concerns often limit the extent of the open sharing of full viral genome sequencing data. We propose a framework called CoVnita, that supports private training of a classification model and secure inference with the same model. Using genomic sequences from eight common SARS-CoV-2 strains, we simulated scenarios where the data was distributed across multiple data providers. Our framework produces a private federated model, over 8 parties, with a classification AUROC of 0.99, given a privacy budget of [Formula: see text]. The roundtrip time, from encryption to decryption, took a total of 0.298 s, with an amortized time of 74.5 ms per sample.

Managing Security of Healthcare Data for a Modern Healthcare System.

The advent of Artificial Intelligence (AI) and the Internet of Things (IoT) have recently created previously unimaginable opportunities for boosting clinical and patient services, reducing costs and improving community health. Yet, a fundamental challenge that the modern healthcare management system faces is storing and securely transferring data. Therefore, this research proposes a novel Lionized remora optimization-based serpent (LRO-S) encryption method to encrypt sensitive data and reduce privacy breaches and cyber-attacks from unauthorized users and hackers. The LRO-S method is the combination of hybrid metaheuristic optimization and improved security algorithm. The fitness functions of lion and remora are combined to create a new algorithm for security key generation, which is provided to the serpent encryption algorithm. The LRO-S technique encrypts sensitive patient data before storing it in the cloud. The primary goal of this study is to improve the safety and adaptability of medical professionals' access to cloud-based patient-sensitive data more securely. The experiment's findings suggest that the secret keys generated are sufficiently random and one of a kind to provide adequate protection for the data stored in modern healthcare management systems. The proposed method minimizes the time needed to encrypt and decrypt data and improves privacy standards. This study found that the suggested technique outperformed previous techniques in terms of reducing execution time and is cost-effective.

Assessing the Governance of Digital Contact Tracing in Response to COVID-19: Results of a Multi-National Study.

This paper describes the results of a multi-country survey of governance approaches for the use of digital contact tracing (DCT) in response to the COVID-19 pandemic. We argue that the countries in our survey represent two distinct models of DCT governance, both of which are flawed. The "data protection model" emphasizes privacy protections at the expense of public health benefit, while the "emergency response model" sacrifices transparency and accountability, prompting concerns about excessive governance surveillance. The ethical and effective use of DCT in the future requires a new governance approach that is better suited to this novel use of mobile phone data to promote public health."

Ethical, Legal, Organisational and Social Issues of Teleneurology: A Scoping Review.

BACKGROUND: Neurological disorders are the leading cause of disability and the second leading cause of death worldwide. Teleneurology (TN) allows neurology to be applied when the doctor and patient are not present in the same place, and sometimes not at the same time. In February 2021, the Spanish Ministry of Health requested a health technology assessment report on the implementation of TN as a complement to face-to-face neurological care. METHODS: A scoping review was conducted to answer the question on the ethical, legal, social, organisational, patient (ELSI) and environmental impact of TN. The assessment of these aspects was carried out by adapting the EUnetHTA Core Model 3.0 framework, the criteria established by the Spanish Network of Health Technology Assessment Agencies and the analysis criteria of the European Validate (VALues In Doing Assessments of healthcare TEchnologies) project. Key stakeholders were invited to discuss their concerns about TN in an online meeting. Subsequently, the following electronic databases were consulted from 2016 to 10 June 2021: MEDLINE and EMBASE. RESULTS: 79 studies met the inclusion criteria. This scoping review includes 37 studies related to acceptability and equity, 15 studies developed during COVID and 1 study on environmental aspects. Overall, the reported results reaffirm the necessary complementarity of TN with the usual face-to-face care. CONCLUSIONS: This need for complementarity relates to factors such as acceptability, feasibility, risk of dehumanisation and aspects related to privacy and the confidentiality of sensitive data.

PrivacyMask: Real-world privacy protection in face ID systems.

Recent works have illustrated that many facial privacy protection methods are effective in specific face recognition algorithms. However, the COVID-19 pandemic has promoted the rapid innovation of face recognition algorithms for face occlusion, especially for the face wearing a mask. It is tricky to avoid being tracked by artificial intelligence only through ordinary props because many facial feature extractors can determine the ID only through a tiny local feature. Therefore, the ubiquitous high-precision camera makes privacy protection worrying. In this paper, we establish an attack method directed against liveness detection. A mask printed with a textured pattern is proposed, which can resist the face extractor optimized for face occlusion. We focus on studying the attack efficiency in adversarial patches mapping from two-dimensional to three-dimensional space. Specifically, we investigate a projection network for the mask structure. It can convert the patches to fit perfectly on the mask. Even if it is deformed, rotated and the lighting changes, it will reduce the recognition ability of the face extractor. The experimental results show that the proposed method can integrate multiple types of face recognition algorithms without significantly reducing the training performance. If we combine it with the static protection method, people can prevent face data from being collected.

COVID-19 Health Passes: Practical and Ethical Issues.

Several countries have implemented COVID-19 health passes or certificates to promote a safer return to in-person social activities. These passes have been proposed as a way to prove that someone has been vaccinated, has recovered from the disease, or has negative results on a diagnostic test. However, many people have questioned their ethical justification. This article presents some practical and ethical problems to consider in the event of wishing to implement these passes. Among the former, it is questioned how accurate diagnostic tests are as a means of ensuring that a person is not contagious, whether vaccination guarantees immunity, the fact that health passes can be forged, whether they encourage vaccination, and the problem that there is no universally recognized health pass. Among the ethical issues, it is discussed whether health passes promote discrimination and inequality and whether they violate rights to privacy and freedom. It is concluded that health passes have enough ethical justification to be implemented.

Statistical biases due to anonymization evaluated in an open clinical dataset from COVID-19 patients.

Anonymization has the potential to foster the sharing of medical data. State-of-the-art methods use mathematical models to modify data to reduce privacy risks. However, the degree of protection must be balanced against the impact on statistical properties. We studied an extreme case of this trade-off: the statistical validity of an open medical dataset based on the German National Pandemic Cohort Network (NAPKON), which was prepared for publication using a strong anonymization procedure. Descriptive statistics and results of regression analyses were compared before and after anonymization of multiple variants of the original dataset. Despite significant differences in value distributions, the statistical bias was found to be small in all cases. In the regression analyses, the median absolute deviations of the estimated adjusted odds ratios for different sample sizes ranged from 0.01 [minimum = 0, maximum = 0.58] to 0.52 [minimum = 0.25, maximum = 0.91]. Disproportionate impact on the statistical properties of data is a common argument against the use of anonymization. Our analysis demonstrates that anonymization can actually preserve validity of statistical results in relatively low-dimensional data.

Telemedicine in Brazil: threats to the protection of personal data due to both easing of pandemic measures and poor regulation / Telemedicina no Brasil: ameaças à proteção de dados pessoais em decorrência da flexibilização da pandemia e da regulamentação precária

O presente trabalho analisou os riscos envolvidos na utilização dos recursos de telessaúde e telemedicina, autorizados durante a pandemia de covid-19, sem um correspondente amadurecimento com relação aos requisitos necessários para garantir a segurança dos dados pessoais e dados pessoais sensíveis de seus usuários, seja pela recente entrada em vigor da Lei n. 13.709/2018, seja pela incipiente criação da Autoridade Nacional de Proteção de Dados, que ainda caminha no sentido de se estruturar organicamente. Sob o lume da metodologia civil-constitucional capitaneada por Perlingieri, o artigo destacou a necessidade de que os requisitos tecnológicos abarcados nas relações privadas sejam devidamente adequados aos valores intrínsecos àqueles delineados no texto constitucional, tendo as normas de direito civil como importante vetor na garantia de tal aplicação. A partir de pesquisa qualitativa, valendo-se de fontes indiretas, inclusive legislação estrangeira, e análise à luz da metodologia dedutiva, elencou-se uma série de considerações para a aplicação de recursos da telemedicina no Brasil de maneira adequada e em sintonia com a proteção de dados pessoais de seus cidadãos.

The present work analyzed the risks involved in the use of telehealth and telemedicine resources, authorized during the covid-19 pandemic, without a corresponding maturity in relation to the necessary requirements to guarantee the security of personal data and sensitive personal data of users, whether by the recent entry into force of Law no. 13,709/2018, or the incipient creation of the National Data Protection Authority, which is still moving towards an organic structure. Under the light of the civilconstitutional methodology led by Perlingieri, the article highlights the need for technological requirements encompassed in private relations to be duly adapted to the intrinsic values of those outlined in the constitutional text, with the norms of civil law as an important vector in guaranteeing such an application. Based on qualitative research, using indirect sources, including foreign legislation, and analysis in the light of deductive methodology, a series of considerations are listed for the application of telemedicine resources in Brazil in an adequate manner and in line with the protection of personal data of citizens.

Test, Trace, and Put on the Blockchain?: A Viewpoint Evaluating the Use of Decentralized Systems for Algorithmic Contact Tracing to Combat a Global Pandemic.

The enormous pressure of the increasing case numbers experienced during the COVID-19 pandemic has given rise to a variety of novel digital systems designed to provide solutions to unprecedented challenges in public health. The field of algorithmic contact tracing, in particular, an area of research that had previously received limited attention, has moved into the spotlight as a crucial factor in containing the pandemic. The use of digital tools to enable more robust and expedited contact tracing and notification, while maintaining privacy and trust in the data generated, is viewed as key to identifying chains of transmission and close contacts, and, consequently, to enabling effective case investigations. Scaling these tools has never been more critical, as global case numbers have exceeded 100 million, as many asymptomatic patients remain undetected, and as COVID-19 variants begin to emerge around the world. In this context, there is increasing attention on blockchain technology as a part of systems for enhanced digital algorithmic contact tracing and reporting. By analyzing the literature that has emerged from this trend, the common characteristics of the designs proposed become apparent. An archetypal system architecture can be derived, taking these characteristics into consideration. However, assessing the utility of this architecture using a recognized evaluation framework shows that the added benefits and features of blockchain technology do not provide significant advantages over conventional centralized systems for algorithmic contact tracing and reporting. From our study, it, therefore, seems that blockchain technology may provide a more significant benefit in other areas of public health beyond contact tracing.

Do the Right Thing: A Privacy Policy Adherence Analysis of over Two Million Apps in Apple iOS App Store.

Mobile app developers are often obliged by regulatory frameworks to provide a privacy policy in natural comprehensible language to describe their apps' privacy practices. However, prior research has revealed that: (1) not all app developers offer links to their privacy policies; and (2) even if they do offer such access, it is difficult to determine if it is a valid link to a (valid) policy. While many prior studies looked at this issue in Google Play Store, Apple App Store, and particularly the iOS store, is much less clear. In this paper, we conduct the first and the largest study to investigate the previous issues in the iOS app store ecosystem. First, we introduce an App Privacy Policy Extractor (APPE), a system that embraces and analyses the metadata of over two million apps to give insightful information about the distribution of the supposed privacy policies, and the content of the provided privacy policy links, store-wide. The result shows that only 58.5% of apps provide links to purported privacy policies, while 39.3% do not provide policy links at all. Our investigation of the provided links shows that only 38.4% of those links were directed to actual privacy policies, while 61.6% failed to lead to a privacy policy. Further, for research purposes we introduce the App Privacy Policy Corpus (APPC-451K); the largest app privacy policy corpus consisting of data relating to more than 451K verified privacy policies.

COVID-19 Vehicle Based on an Efficient Mutual Authentication Scheme for 5G-Enabled Vehicular Fog Computing.

The COVID-19 pandemic is currently having disastrous effects on every part of human life everywhere in the world. There have been terrible losses for the entire human race in all nations and areas. It is crucial to take good precautions and prevent COVID-19 because of its high infectiousness and fatality rate. One of the key spreading routes has been identified to be transportation systems. Therefore, improving infection tracking and healthcare monitoring for high-mobility transportation systems is impractical for pandemic control. In order to enhance driving enjoyment and road safety, 5G-enabled vehicular fog computing may gather and interpret pertinent vehicle data, which open the door to non-contact autonomous healthcare monitoring. Due to the urgent need to contain the automotive pandemic, this paper proposes a COVID-19 vehicle based on an efficient mutual authentication scheme for 5G-enabled vehicular fog computing. The proposed scheme consists of two different aspects of the special flag, SF = 0 and SF = 1, denoting normal and COVID-19 vehicles, respectively. The proposed scheme satisfies privacy and security requirements as well as achieves COVID-19 and healthcare solutions. Finally, the performance evaluation section shows that the proposed scheme is more efficient in terms of communication and computation costs as compared to most recent related works.

Blockchain and homomorphic encryption based privacy-preserving model aggregation for medical images.

Medical healthcare centers are envisioned as a promising paradigm to handle the massive volume of data for COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques require centralized data collection and training models within a single organization. This practice can be considered a weakness as it leads to several privacy and security concerns related to raw data communication. To overcome this weakness and secure raw data communication, we propose a blockchain-based federated learning framework that provides a solution for collaborative data training. The proposed framework enables the coordination of multiple hospitals to train and share encrypted federated models while preserving data privacy. Blockchain ledger technology provides decentralization of federated learning models without relying on a central server. Moreover, the proposed homomorphic encryption scheme encrypts and decrypts the gradients of the model to preserve privacy. More precisely, the proposed framework: (i) train the local model by a novel capsule network for segmentation and classification of COVID-19 images, (ii) furthermore, we use the homomorphic encryption scheme to secure the local model that encrypts and decrypts the gradients, (iii) finally, the model is shared over a decentralized platform through the proposed blockchain-based federated learning algorithm. The integration of blockchain and federated learning leads to a new paradigm for medical image data sharing over the decentralized network. To validate our proposed model, we conducted comprehensive experiments and the results demonstrate the superior performance of the proposed scheme.

An Improved IoT-Based System for Detecting the Number of People and Their Distribution in a Classroom.

This paper presents an improved IoT-based system designed to help teachers handle lessons in the classroom in line with COVID-19 restrictions. The system counts the number of people in the classroom as well as their distribution within the classroom. The proposed IoT system consists of three parts: a Gate node, IoT nodes, and server. The Gate node, installed at the door, can provide information about the number of persons entering or leaving the room using door crossing detection. The Arduino-based module NodeMCU was used as an IoT node and sets of ultrasonic distance sensors were used to obtain information about seat occupancy. The system server runs locally on a Raspberry Pi and the teacher can connect to it using a web application from the computer in the classroom or a smartphone. The teacher is able to set up and change the settings of the system through its GUI. A simple algorithm was designed to check the distance between occupied seats and evaluate the accordance with imposed restrictions. This system can provide high privacy, unlike camera-based systems.

Trading-off privacy and utility: the Washington State experience assessing the performance of a public health digital exposure notification system for coronavirus disease 2019.

OBJECTIVE: Digital exposure notifications (DEN) systems were an emergency response to the coronavirus disease 2019 (COVID-19) pandemic, harnessing smartphone-based technology to enhance conventional pandemic response strategies such as contact tracing. We identify and describe performance measurement constructs relevant to the implementation of DEN tools: (1) reach (number of users enrolled in the intervention); (2) engagement (utilization of the intervention); and (3) effectiveness in preventing transmissions of COVID-19 (impact of the intervention). We also describe WA State's experience utilizing these constructs to design data-driven evaluation approaches. METHODS: We conducted an environmental scan of DEN documentation and relevant publications. Participation in multidisciplinary collaborative environments facilitated shared learning. Compilation of available data sources and their relevance to implementation and operation workflows were synthesized to develop implementation evaluation constructs. RESULTS: We identified 8 useful performance indicators within reach, engagement, and effectiveness constructs. DISCUSSION: We use implementation science to frame the evaluation of DEN tools by linking the theoretical constructs with the metrics available in the underlying disparate, deidentified, and aggregate data infrastructure. Our challenges in developing meaningful metrics include limited data science competencies in public health, validation of analytic methodologies in the complex and evolving pandemic environment, and the lack of integration with the public health infrastructure. CONCLUSION: Continued collaboration and multidisciplinary consensus activities can improve the utility of DEN tools for future public health emergencies.

Privacy Concerns and Continued Use Intention of Telemedicine During COVID-19.

Introduction: Privacy concerns are a major barrier to online technology adoption. However, when consumers are facing personal risks (being ill) and environmental risks (pandemic), the effect of privacy concerns on continued use intention of telemedicine is unknown. The large user pool of virtual visits during COVID-19 provides a great opportunity to understand consumers' privacy concerns when facing personal and environmental risks. Objective: This research investigates how patients weigh personal risks (e.g., illness) and environmental risks (e.g., pandemic) against privacy concerns when deciding whether to utilize telemedicine as an option for being treated for an acute illness. Methods: Respondents (1,059 qualified) meeting the following criteria: ≥18 years old, U.S. residents, virtual patient for acute conditions during COVID-19, and a Human Intelligence Task approval rate of >95%, were recruited utilizing Amazon Mechanical Turk during the middle of the pandemic. An online survey was conducted to collect data. Results: Analysis indicates that first-time telepatients (82% of respondents) have greater privacy concerns than repeat users. Findings also indicate that patients who are female and have some college education or less reported greater privacy concerns. Interestingly, privacy concerns are positively related to continued use intention. This result holds when satisfaction and user characteristics are controlled. Conclusions: When consumers are ill, privacy concerns still play an important role in telemedicine adoption. However, under environmental risks such as the COVID-19 pandemic, privacy concerns do not negatively impact their continued use intention, and satisfaction is positively associated with continued use intention.