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Travel Medicine and Infectious Disease 2021The advent of mobile applications for health and medicine will revolutionize travel medicine. Despite their many benefits, such as access to real-time data, mobile apps... (Review)
Review
BACKGROUND
The advent of mobile applications for health and medicine will revolutionize travel medicine. Despite their many benefits, such as access to real-time data, mobile apps for travel medicine are accompanied by many ethical issues, including questions about security and privacy.
METHODS
A systematic literature review as conducted following PRISMA guidelines. Database screening yielded 1795 results and seven papers satisfied the criteria for inclusion. Through a mix of inductive and deductive data extraction, this systematic review examined both the benefits and challenges, as well as ethical considerations, of mobile apps for travel medicine.
RESULTS
Ethical considerations were discussed with varying depth across the included articles, with privacy and data protection mentioned most frequently, highlighting concerns over sensitive information and a lack of guidelines in the digital sphere. Additionally, technical concerns about data quality and bias were predominant issues for researchers and developers alike. Some ethical issues were not discussed at all, including equity, and user involvement.
CONCLUSION
This paper highlights the scarcity of discussion around ethical issues. Both researchers and developers need to better integrate ethical reflection at each step of the development and use of health apps. More effective oversight mechanisms and clearer ethical guidance are needed to guide the stakeholders in this endeavour.
Topics: Humans; Mobile Applications; Privacy; Travel Medicine
PubMed: 34256131
DOI: 10.1016/j.tmaid.2021.102143 -
Sensors (Basel, Switzerland) May 2022With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in... (Review)
Review
With the rapid growth in healthcare demand, an emergent, novel technology called wireless body area networks (WBANs) have become promising and have been widely used in the field of human health monitoring. A WBAN can collect human physical parameters through the medical sensors in or around the patient's body to realize real-time continuous remote monitoring. Compared to other wireless transmission technologies, a WBAN has more stringent technical requirements and challenges in terms of power efficiency, security and privacy, quality of service and other specifications. In this paper, we review the recent WBAN medical applications, existing requirements and challenges and their solutions. We conducted a comprehensive investigation of WBANs, from the sensor technology for the collection to the wireless transmission technology for the transmission process, such as frequency bands, channel models, medium access control (MAC) and networking protocols. Then we reviewed its unique safety and energy consumption issues. In particular, an application-specific integrated circuit (ASIC)-based WBAN scheme is presented to improve its security and privacy and achieve ultra-low energy consumption.
Topics: Computer Communication Networks; Humans; Privacy; Technology; Wireless Technology
PubMed: 35591234
DOI: 10.3390/s22093539 -
Sensors (Basel, Switzerland) Feb 2022Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization,...
Currently, personal data collection and processing are widely used while providing digital services within mobile sensing networks for their operation, personalization, and improvement. Personal data are any data that identifiably describe a person. Legislative and regulatory documents adopted in recent years define the key requirements for the processing of personal data. They are based on the principles of lawfulness, fairness, and transparency of personal data processing. Privacy policies are the only legitimate way to provide information on how the personal data of service and device users is collected, processed, and stored. Therefore, the problem of making privacy policies clear and transparent is extremely important as its solution would allow end users to comprehend the risks associated with personal data processing. Currently, a number of approaches for analyzing privacy policies written in natural language have been proposed. Most of them require a large training dataset of privacy policies. In the paper, we examine the existing corpora of privacy policies available for training, discuss their features and conclude on the need for a new dataset of privacy policies for devices and services of the Internet of Things as a part of mobile sensing networks. The authors develop a new technique for collecting and cleaning such privacy policies. The proposed technique differs from existing ones by the usage of e-commerce platforms as a starting point for document search and enables more targeted collection of the URLs to the IoT device manufacturers' privacy policies. The software tool implementing this technique was used to collect a new corpus of documents in English containing 592 unique privacy policies. The collected corpus contains mainly privacy policies that are developed for the Internet of Things and reflect the latest legislative requirements. The paper also presents the results of the statistical and semantic analysis of the collected privacy policies. These results could be further used by the researchers when elaborating techniques for analysis of the privacy policies written in natural language targeted to enhance their transparency for the end user.
Topics: Data Collection; Humans; Policy; Privacy
PubMed: 35270993
DOI: 10.3390/s22051838 -
Computational Intelligence and... 2022The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning...
The privacy protection for face images aims to prevent attackers from accurately identifying target persons through face recognition. Inspired by goal-driven reasoning (reverse reasoning), this paper designs a goal-driven algorithm of local privacy protection for sensitive areas in multiface images (face areas) under the interactive framework of face recognition algorithm, regional growth, and differential privacy. The designed algorithm, named privacy protection for sensitive areas (PPSA), is realized in the following manner: Firstly, the multitask cascaded convolutional network (MTCNN) was adopted to recognize the region and landmark of each face. If the landmark overlaps a subgraph divided from the original image, the subgraph will be taken as the seed for regional growth in the face area, following the growth criterion of the fusion similarity measurement mechanism (FSMM). Different from single-face privacy protection, multiface privacy protection needs to deal with an unknown number of faces. Thus, the allocation of the privacy budget directly affects the operation effect of the PPSA algorithm. In our scheme, the total privacy budget is divided into two parts: _1 and _2. The former is evenly allocated to each seed, according to the estimated number of faces contained in the image, while the latter is allocated to the other areas that may consume the privacy budget through dichotomization. Unlike the Laplacian (LAP) algorithm, the noise error of the PPSA algorithm will not change with the image size, for the privacy protection is limited to the face area. The results show that the PPSA algorithm meets the requirements -Differential privacy, and image classification is realized by using different image privacy protection algorithms in different human face databases. The verification results show that the accuracy of the PPSA algorithm is improved by at least 16.1%, the recall rate is improved by at least 2.3%, and 1-score is improved by at least 15.2%.
Topics: Algorithms; Databases, Factual; Facial Recognition; Head; Humans; Privacy
PubMed: 35330598
DOI: 10.1155/2022/5919522 -
BMC Medical Informatics and Decision... Apr 2022Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and...
BACKGROUND
Logistic regression (LR) is a widely used classification method for modeling binary outcomes in many medical data classification tasks. Researchers that collect and combine datasets from various data custodians and jurisdictions can greatly benefit from the increased statistical power to support their analysis goals. However, combining data from different sources creates serious privacy concerns that need to be addressed.
METHODS
In this paper, we propose two privacy-preserving protocols for performing logistic regression with the Newton-Raphson method in the estimation of parameters. Our proposals are based on secure Multi-Party Computation (MPC) and tailored to the honest majority and dishonest majority security settings.
RESULTS
The proposed protocols are evaluated against both synthetic and real-world datasets in terms of efficiency and accuracy, and a comparison is made with the ordinary logistic regression. The experimental results demonstrate that the proposed protocols are highly efficient and accurate.
CONCLUSIONS
Our work introduces two iterative algorithms to enable the distributed training of a logistic regression model in a privacy-preserving manner. The implementation results show that our algorithms can handle large datasets from multiple sources.
Topics: Algorithms; Humans; Logistic Models; Privacy
PubMed: 35366870
DOI: 10.1186/s12911-022-01811-y -
Genome Biology Sep 2023Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in...
Growing regulatory requirements set barriers around genetic data sharing and collaborations. Moreover, existing privacy-aware paradigms are challenging to deploy in collaborative settings. We present COLLAGENE, a tool base for building secure collaborative genomic data analysis methods. COLLAGENE protects data using shared-key homomorphic encryption and combines encryption with multiparty strategies for efficient privacy-aware collaborative method development. COLLAGENE provides ready-to-run tools for encryption/decryption, matrix processing, and network transfers, which can be immediately integrated into existing pipelines. We demonstrate the usage of COLLAGENE by building a practical federated GWAS protocol for binary phenotypes and a secure meta-analysis protocol. COLLAGENE is available at https://zenodo.org/record/8125935 .
Topics: Data Analysis; Genomics; Information Dissemination; Phenotype; Privacy; Meta-Analysis as Topic
PubMed: 37697426
DOI: 10.1186/s13059-023-03039-z -
Health Care Analysis : HCA : Journal of... Mar 2022Information is clearly vital to public health, but the acquisition and use of public health data elicit serious privacy concerns. One strategy for navigating this...
Information is clearly vital to public health, but the acquisition and use of public health data elicit serious privacy concerns. One strategy for navigating this dilemma is to build 'trust' in institutions responsible for health information, thereby reducing privacy concerns and increasing willingness to contribute personal data. This strategy, as currently presented in public health literature, has serious shortcomings. But it can be augmented by appealing to the philosophical analysis of the concept of trust. Philosophers distinguish trust and trustworthiness from cognate attitudes, such as confident reliance. Central to this is value congruence: trust is grounded in the perception of shared values. So, the way to build trust in institutions responsible for health data is for those institutions to develop and display values shared by the public. We defend this approach from objections, such as that trust is an interpersonal attitude inappropriate to the way people relate to organisations. The paper then moves on to the practical application of our strategy. Trust and trustworthiness can reduce privacy concerns and increase willingness to share health data, notably, in the context of internal and external threats to data privacy. We end by appealing for the sort of empirical work our proposal requires.
Topics: Attitude; Humans; Privacy; Public Health; Trust
PubMed: 34751865
DOI: 10.1007/s10728-021-00436-y -
Sensors (Basel, Switzerland) Dec 2022The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a... (Review)
Review
The continuously increasing number of mobile devices actively being used in the world amounted to approximately 6.8 billion by 2022. Consequently, this implies a substantial increase in the amount of personal data collected, transported, processed, and stored. The authors of this paper designed and implemented an integrated personal health data management system, which considers data-driven software and hardware sensors, comprehensive data privacy techniques, and machine-learning-based algorithmic models. It was determined that there are very few relevant and complete surveys concerning this specific problem. Therefore, the current scientific research was considered, and this paper comprehensively analyzes the importance of deep learning techniques that are applied to the overall management of data collected by data-driven soft sensors. This survey considers aspects that are related to demographics, health and body parameters, and human activity and behaviour pattern detection. Additionally, the relatively complex problem of designing and implementing data privacy mechanisms, while ensuring efficient data access, is also discussed, and the relevant metrics are presented. The paper concludes by presenting the most important open research questions and challenges. The paper provides a comprehensive and thorough scientific literature survey, which is useful for any researcher or practitioner in the scope of data-driven soft sensors and privacy techniques, in relation to the relevant machine-learning-based models.
Topics: Humans; Privacy; Deep Learning; Software
PubMed: 36616892
DOI: 10.3390/s23010294 -
Sensors (Basel, Switzerland) Apr 2022Most traditional agricultural traceability systems are centralized, which could result in the low reliability of traceability results, enterprise privacy data leakage...
Most traditional agricultural traceability systems are centralized, which could result in the low reliability of traceability results, enterprise privacy data leakage vulnerabilities, and the generation of information islands. To solve the above problems, we propose a trusted agricultural product traceability system based on the Ethereum blockchain in this paper. We designed a dual storage model of "Blockchain+IPFS (InterPlanetary File System)" to reduce the storage pressure of the blockchain and realize efficient information queries. Additionally, we propose a data privacy protection solution based on some cryptographic primitives and the Merkle Tree that can avoid enterprise privacy and sensitive data leakage. Furthermore, we implemented the proposed system using the Ethereum blockchain platform and provided the cost, performance, and security analysis, as well as compared it with the existing solutions. The results showed that the proposed system is both efficient and feasible and can meet the practical application requirements.
Topics: Blockchain; Computer Security; Privacy; Reproducibility of Results
PubMed: 35591077
DOI: 10.3390/s22093388 -
Frontiers in Public Health 2020The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care,...
The scarcity of medical resources is a fundamental problem worldwide; the development of information technology and the Internet has given birth to online health care, which has alleviated the above problem. The survival and sustainable development of the online health community requires users to continuously disclose their health and privacy. Therefore, it is a great practical significance to find out the factors and mechanisms that promote users' self-disclosure in the online health community. From the perspective of individual and situation interaction, this study constructed influencing factors model of health privacy information self-disclosure. Finally, we collected 264 valid samples from the online health community through online and offline questionnaire surveys and then use the SPSS20.0 and AMOS21.0 to conduct exploratory factor analysis, confirmatory factor analysis, scale reliability and validity analysis, and structural equation model analysis. The main findings are as follows: trust in websites and trust in doctors reduce the privacy concern. The privacy trade-off will not occur when trust is enough to offset the privacy concerns caused by personalized services, reciprocity norms, and other factors. Second, reciprocity norms are inevitably compulsive, which will increase privacy concerns. However, based on voluntariness, reciprocity norms can enhance user trust. Third, service quality caused by personalized services not only enhance the social rewards of users but also eliminate the privacy concern. Fourth, users' health privacy attention and information sensitivity are too high to decrease the influence of user' privacy concerns on personal health privacy information disclosure. The conclusions of this paper will help us to supplement privacy calculus theory and the application scope of the attention-based view. The proposed strategy of this article can be used to stimulate the information contribution behavior of users and improve the medical service capabilities in online health community.
Topics: Disclosure; Health Records, Personal; Privacy; Reproducibility of Results; Self Disclosure
PubMed: 33614566
DOI: 10.3389/fpubh.2020.602792