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International Journal of Environmental... Sep 2022This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most... (Review)
Review
This article offers a brief overview of 'privacy-by-design (or data-protection-by-design) research environments', namely Trusted Research Environments (TREs, most commonly used in the United Kingdom) and Personal Health Trains (PHTs, most commonly used in mainland Europe). These secure environments are designed to enable the safe analysis of multiple, linked (and often big) data sources, including sensitive personal data and data owned by, and distributed across, different institutions. They take data protection and privacy requirements into account from the very start (conception phase, during system design) rather than as an afterthought or 'patch' implemented at a later stage on top of an existing environment. TREs and PHTs are becoming increasingly important for conducting large-scale privacy-preserving health research and for enabling federated learning and discoveries from big healthcare datasets. The paper also presents select examples of successful TRE and PHT implementations and of large-scale studies that used them.
Topics: Computer Security; Delivery of Health Care; Europe; Information Storage and Retrieval; Privacy
PubMed: 36231175
DOI: 10.3390/ijerph191911876 -
Yearbook of Medical Informatics Aug 2023Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from...
OBJECTIVES
Machine learning (ML) is a powerful asset to support physicians in decision-making procedures, providing timely answers. However, ML for health systems can suffer from security attacks and privacy violations. This paper investigates studies of security and privacy in ML for health.
METHODS
We examine attacks, defenses, and privacy-preserving strategies, discussing their challenges. We conducted the following research protocol: starting a manual search, defining the search string, removing duplicated papers, filtering papers by title and abstract, then their full texts, and analyzing their contributions, including strategies and challenges. Finally, we collected and discussed 40 papers on attacks, defense, and privacy.
RESULTS
Our findings identified the most employed strategies for each domain. We found trends in attacks, including universal adversarial perturbation (UAPs), generative adversarial network (GAN)-based attacks, and DeepFakes to generate malicious examples. Trends in defense are adversarial training, GAN-based strategies, and out-of-distribution (OOD) to identify and mitigate adversarial examples (AE). We found privacy-preserving strategies such as federated learning (FL), differential privacy, and combinations of strategies to enhance the FL. Challenges in privacy comprehend the development of attacks that bypass fine-tuning, defenses to calibrate models to improve their robustness, and privacy methods to enhance the FL strategy.
CONCLUSIONS
In conclusion, it is critical to explore security and privacy in ML for health, because it has grown risks and open vulnerabilities. Our study presents strategies and challenges to guide research to investigate issues about security and privacy in ML applied to health systems.
Topics: Humans; Privacy; Machine Learning; Physicians
PubMed: 38147869
DOI: 10.1055/s-0043-1768731 -
Human Molecular Genetics Oct 2021Debates surrounding genetic privacy have taken on different forms over the past 30 years. Taking genetic privacy to mean an interest that individuals, families, or even... (Review)
Review
Debates surrounding genetic privacy have taken on different forms over the past 30 years. Taking genetic privacy to mean an interest that individuals, families, or even communities have with respect to genetic information, we examine the metaphors used in these debates to chronicle the development of genetic privacy. In 1990-2000, we examine claims for ownership and of 'humanity' spurred by the launch of the Human Genome Project and related endeavors. In 2000-2010, we analyze the interface of law and ethics with research infrastructures such as biobanks, for which notions of citizenship and 'public goods' were central. In 2010-2020, we detail the relational turn of genetic privacy in response of large international research consortia and big data. Although each decade had its leading conceptions of genetic privacy, the subject is neither strictly chronological nor static. We conclude with reflections on the nature of genetic privacy and the necessity to bring together the unique and private genetic self with the human other.
Topics: Ethics, Clinical; Genetic Privacy; Human Genome Project; Humanities; Humans; Ownership
PubMed: 34155499
DOI: 10.1093/hmg/ddab164 -
Current Opinion in Psychology Feb 2020Communication Privacy Management (CPM) theory explains one of the most important, yet challenging social processes in everyday life, that is, managing disclosing and... (Review)
Review
Communication Privacy Management (CPM) theory explains one of the most important, yet challenging social processes in everyday life, that is, managing disclosing and protecting private information. The CPM privacy management system offers researchers, students, and the public a comprehensive approach to the complex and fluid character of privacy management in action. Following an overview of Communication Privacy Management framework, this review focuses on recent research utilizing CPM concepts that cross a growing number of contexts and illustrates the way people navigate privacy in action. Researchers operationalize the use of privacy rules and other core concepts that help describe and explain the ups and downs of privacy management people encounter.
Topics: Communication; Concept Formation; Humans; Privacy; Psychological Theory; Research
PubMed: 31526974
DOI: 10.1016/j.copsyc.2019.08.009 -
Sensors (Basel, Switzerland) Jun 2021In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing... (Review)
Review
In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.
Topics: Female; Humans; Male; Privacy; Technology; Workplace
PubMed: 34209327
DOI: 10.3390/s21134501 -
Sensors (Basel, Switzerland) Aug 2022As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable... (Review)
Review
As smart devices and mobile positioning technologies improve, location-based services (LBS) have grown in popularity. The LBS environment provides considerable convenience to users, but it also poses a significant threat to their privacy. A large number of research works have emerged to protect users' privacy. Dummy-based location privacy protection solutions have been widely adopted for their simplicity and enhanced privacy protection results, but there are few reviews on dummy-based location privacy protection. Or, for existing works, some focus on aspects of cryptography, anonymity, or other comprehensive reviews that do not provide enough reviews on dummy-based privacy protection. In this paper, the authors provide a review of dummy-based location privacy protection techniques for location-based services. More specifically, the connection between the level of privacy protection, the quality of service, and the system overhead is summarized. The difference and connection between various location privacy protection techniques are also described. The dummy-based attack models are presented. Then, the algorithms for dummy location selection are analyzed and evaluated. Finally, we thoroughly evaluate different dummy location selection methods and arrive at a highly useful evaluation result. This result is valuable both to users and researchers who are studying this field.
Topics: Algorithms; Computer Security; Privacy
PubMed: 36015901
DOI: 10.3390/s22166141 -
Sensors (Basel, Switzerland) Jan 2022Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising... (Review)
Review
Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services closer to data sources. EC combined with Deep Learning (DL) is a promising technology and is widely used in several applications. However, in conventional DL architectures with EC enabled, data producers must frequently send and share data with third parties, edge or cloud servers, to train their models. This architecture is often impractical due to the high bandwidth requirements, legalization, and privacy vulnerabilities. The Federated Learning (FL) concept has recently emerged as a promising solution for mitigating the problems of unwanted bandwidth loss, data privacy, and legalization. FL can co-train models across distributed clients, such as mobile phones, automobiles, hospitals, and more, through a centralized server, while maintaining data localization. FL can therefore be viewed as a stimulating factor in the EC paradigm as it enables collaborative learning and model optimization. Although the existing surveys have taken into account applications of FL in EC environments, there has not been any systematic survey discussing FL implementation and challenges in the EC paradigm. This paper aims to provide a systematic survey of the literature on the implementation of FL in EC environments with a taxonomy to identify advanced solutions and other open problems. In this survey, we review the fundamentals of EC and FL, then we review the existing related works in FL in EC. Furthermore, we describe the protocols, architecture, framework, and hardware requirements for FL implementation in the EC environment. Moreover, we discuss the applications, challenges, and related existing solutions in the edge FL. Finally, we detail two relevant case studies of applying FL in EC, and we identify open issues and potential directions for future research. We believe this survey will help researchers better understand the connection between FL and EC enabling technologies and concepts.
Topics: Cloud Computing; Forecasting; Humans; Privacy
PubMed: 35062410
DOI: 10.3390/s22020450 -
Current Pharmaceutical Design 2021Adverse drug events have been a long-standing concern for the wide-ranging harms to public health, and the substantial disease burden. The key to diminish or eliminate...
Adverse drug events have been a long-standing concern for the wide-ranging harms to public health, and the substantial disease burden. The key to diminish or eliminate the impacts is to build a comprehensive pharmacovigilance system. Application of the "big data" approach has been proved to assist the detection of adverse drug events by involving previously unavailable data sources and promoting health information exchange. Even though challenges and potential risks still remain. The lack of effective privacy-preserving measures in the flow of medical data is the most important Accepted: one, where urgent actions are required to prevent the threats and facilitate the construction of pharmacovigilance systems. Several privacy protection methods are reviewed in this article, which may be helpful to break the barrier.
Topics: Big Data; Humans; Information Dissemination; Pharmaceutical Preparations; Privacy
PubMed: 33438533
DOI: 10.2174/1381612827999210112204732 -
Social Studies of Science Dec 2022The European Union's General Data Protection Regulation (GDPR), in force since 2018, has introduced design-based approaches to data protection and the governance of...
The European Union's General Data Protection Regulation (GDPR), in force since 2018, has introduced design-based approaches to data protection and the governance of privacy. In this article we describe the emergence of the professional field of privacy engineering to enact this shift in digital governance. We argue that privacy engineering forms part of a broader techno-regulatory imaginary through which (fundamental) rights protections become increasingly future-oriented and anticipatory. The techno-regulatory imaginary is described in terms of three distinct privacy articulations, implemented in technologies, organizations, and standardizations. We pose two interrelated questions: What happens to rights as they become implemented and enacted in new sites, through new instruments and professional practices? And, focusing on shifts to the nature of boundary work, we ask: What forms of legitimation can be discerned as privacy engineering is mobilized for the making of future digital markets and infrastructures?
Topics: Privacy; Computer Security; Engineering
PubMed: 36000578
DOI: 10.1177/03063127221119424 -
Journal of Paediatrics and Child Health Mar 2016
Topics: Adolescent; Child; Child Health; Child, Preschool; Confidentiality; Female; Humans; Male; New South Wales; Pediatrics; Physician-Patient Relations; Privacy
PubMed: 27124837
DOI: 10.1111/jpc.13143