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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 -
International Journal of Environmental... Sep 2021Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system.... (Review)
Review
Recently, the integration of state-of-the-art technologies, such as modern sensors, networks, and cloud computing, has revolutionized the conventional healthcare system. However, security concerns have increasingly been emerging due to the integration of technologies. Therefore, the security and privacy issues associated with e-health data must be properly explored. In this paper, to investigate the security and privacy of e-health systems, we identified major components of the modern e-health systems (i.e., e-health data, medical devices, medical networks and edge/fog/cloud). Then, we reviewed recent security and privacy studies that focus on each component of the e-health systems. Based on the review, we obtained research taxonomy, security concerns, requirements, solutions, research trends, and open challenges for the components with strengths and weaknesses of the analyzed studies. In particular, edge and fog computing studies for e-health security and privacy were reviewed since the studies had mostly not been analyzed in other survey papers.
Topics: Cloud Computing; Computer Security; Delivery of Health Care; Electronic Health Records; Privacy
PubMed: 34574593
DOI: 10.3390/ijerph18189668 -
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 -
International Journal of Law and... 2022Health care organizations are obligated to provide safe and effective treatment to their patients and also protect the safety of their workers. This paper analyzes the...
Health care organizations are obligated to provide safe and effective treatment to their patients and also protect the safety of their workers. This paper analyzes the tensions arising from legislative regimes that, respectively, protect privacy and workplace safety, using a large, tertiary high-secure forensic psychiatric hospital in Ontario, Canada, as an example. In Ontario, the Personal Health Information Protection Act (PHIPA) prohibits personal health information (PHI) from being disclosed to individuals who fall outside the "circle of care," including nonclinical employees who have direct involvement with patients and may be at risk of violence. PHIPA permits the disclosure of information where there is a risk of violence, but the statute's scheme for privacy protection was not designed to address, and may not be compatible with, the operations and requirements of high-secure forensic and other psychiatric hospitals. At the same time, the Occupational Health and Safety Act (OHSA) creates a regulatory framework that sets health and safety standards, including an employer's duty to disclose the risk of violence. OHSA prosecutions and proceedings demonstrate how these duties have been enforced against psychiatric hospitals. We examine this regulatory backdrop, explaining that PHIPA provides little guidance to psychiatric hospitals, where the risk of violence is elevated. We also discuss issues of dual compliance that arise from a hospital's legal obligations under PHIPA and OHSA. Finally, we turn to the ongoing clinical and operational challenges, suggesting strategies for increasing staff safety. These include strengthening the therapeutic alliance and providing patients with the option of consenting to disclosure of PHI to those outside the circle of care.
Topics: Hospitals, Psychiatric; Humans; Ontario; Privacy; Workplace
PubMed: 35279456
DOI: 10.1016/j.ijlp.2022.101780 -
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 -
Sensors (Basel, Switzerland) Dec 2022In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks...
In mobile edge computing (MEC), mobile devices can choose to offload their tasks to edge servers for execution, thereby effectively reducing the completion time of tasks and energy consumption of mobile devices. However, most of the data transfer brought by offloading relies on wireless communication technology, making the private information of mobile devices vulnerable to eavesdropping and monitoring. Privacy leakage, especially the location and association privacies, can pose a significant risk to users of mobile devices. Therefore, protecting the privacy of mobile devices during task offloading is important and cannot be ignored. This paper considers both location privacy and association privacy of mobile devices during task offloading in MEC and targets to reduce the leakage of location and association privacy while minimizing the average completion time of tasks. To achieve these goals, we design a privacy-preserving task offloading scheme to protect location privacy and association privacy. The scheme is mainly divided into two parts. First, we adopt a proxy forwarding mechanism to protect the location privacy of mobile devices from being leaked. Second, we select the proxy server and edge server for each task that needs to be offloaded. In the proxy server selection policy, we make a choice based on the location information of proxy servers, to reduce the leakage risk of location privacy. In the edge server selection strategy, we consider the privacy conflict between tasks, the computing ability, and location of edge servers, to reduce the leakage risk of association privacy plus the average completion time of tasks as much as possible. Simulated experimental results demonstrate that our scheme is effective in protecting the location privacy and association privacy of mobile devices and reducing the average completion time of tasks compared with the-state-of-art techniques.
Topics: Privacy; Communication; Computers, Handheld; Information Technology; Policy
PubMed: 36616692
DOI: 10.3390/s23010095 -
Briefings in Bioinformatics Nov 2022Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to...
BACKGROUND
Estimation of genetic relatedness, or kinship, is used occasionally for recreational purposes and in forensic applications. While numerous methods were developed to estimate kinship, they suffer from high computational requirements and often make an untenable assumption of homogeneous population ancestry of the samples. Moreover, genetic privacy is generally overlooked in the usage of kinship estimation methods. There can be ethical concerns about finding unknown familial relationships in third-party databases. Similar ethical concerns may arise while estimating and reporting sensitive population-level statistics such as inbreeding coefficients for the concerns around marginalization and stigmatization.
RESULTS
Here, we present SIGFRIED, which makes use of existing reference panels with a projection-based approach that simplifies kinship estimation in the admixed populations. We use simulated and real datasets to demonstrate the accuracy and efficiency of kinship estimation. We present a secure federated kinship estimation framework and implement a secure kinship estimator using homomorphic encryption-based primitives for computing relatedness between samples in two different sites while genotype data are kept confidential. Source code and documentation for our methods can be found at https://doi.org/10.5281/zenodo.7053352.
CONCLUSIONS
Analysis of relatedness is fundamentally important for identifying relatives, in association studies, and for estimation of population-level estimates of inbreeding. As the awareness of individual and group genomic privacy is growing, privacy-preserving methods for the estimation of relatedness are needed. Presented methods alleviate the ethical and privacy concerns in the analysis of relatedness in admixed, historically isolated and underrepresented populations.
SHORT ABSTRACT
Genetic relatedness is a central quantity used for finding relatives in databases, correcting biases in genome wide association studies and for estimating population-level statistics. Methods for estimating genetic relatedness have high computational requirements, and occasionally do not consider individuals from admixed ancestries. Furthermore, the ethical concerns around using genetic data and calculating relatedness are not considered. We present a projection-based approach that can efficiently and accurately estimate kinship. We implement our method using encryption-based techniques that provide provable security guarantees to protect genetic data while kinship statistics are computed among multiple sites.
Topics: Humans; Genome-Wide Association Study; Privacy; Genotype; Genetic Privacy; Genome
PubMed: 36384083
DOI: 10.1093/bib/bbac473