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Entropy (Basel, Switzerland) Nov 2023With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues....
With the development of mobile applications, location-based services (LBSs) have been incorporated into people's daily lives and created huge commercial revenues. However, when using these services, people also face the risk of personal privacy breaches due to the release of location and query content. Many existing location privacy protection schemes with centralized architectures assume that anonymous servers are secure and trustworthy. This assumption is difficult to guarantee in real applications. To solve the problem of relying on the security and trustworthiness of anonymous servers, we propose a Geohash-based location privacy protection scheme for snapshot queries. It is named GLPS. On the user side, GLPS uses Geohash encoding technology to convert the user's location coordinates into a string code representing a rectangular geographic area. GLPS uses the code as the privacy location to send check-ins and queries to the anonymous server and to avoid the anonymous server gaining the user's exact location. On the anonymous server side, the scheme takes advantage of Geohash codes' geospatial gridding capabilities and GL-Tree's effective location retrieval performance to generate a -anonymous query set based on user-defined minimum and maximum hidden cells, making it harder for adversaries to pinpoint the user's location. We experimentally tested the performance of GLPS and compared it with three schemes: Casper, GCasper, and DLS. The experimental results and analyses demonstrate that GLPS has a good performance and privacy protection capability, which resolves the reliance on the security and trustworthiness of anonymous servers. It also resists attacks involving background knowledge, regional centers, homogenization, distribution density, and identity association.
PubMed: 38136449
DOI: 10.3390/e25121569 -
Biomedical Materials & Devices (New... Feb 2023Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and... (Review)
Review
Artificial intelligence (AI) has the potential to make substantial progress toward the goal of making healthcare more personalized, predictive, preventative, and interactive. We believe AI will continue its present path and ultimately become a mature and effective tool for the healthcare sector. Besides this AI-based systems raise concerns regarding data security and privacy. Because health records are important and vulnerable, hackers often target them during data breaches. The absence of standard guidelines for the moral use of AI and ML in healthcare has only served to worsen the situation. There is debate about how far artificial intelligence (AI) may be utilized ethically in healthcare settings since there are no universal guidelines for its use. Therefore, maintaining the confidentiality of medical records is crucial. This study enlightens the possible drawbacks of AI in the implementation of healthcare sector and their solutions to overcome these situations.
PubMed: 36785697
DOI: 10.1007/s44174-023-00063-2 -
Advances and Challenges in Drone Detection and Classification Techniques: A State-of-the-Art Review.Sensors (Basel, Switzerland) Dec 2023The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and... (Review)
Review
The fast development of unmanned aerial vehicles (UAVs), commonly known as drones, has brought a unique set of opportunities and challenges to both the civilian and military sectors. While drones have proven useful in sectors such as delivery, agriculture, and surveillance, their potential for abuse in illegal airspace invasions, privacy breaches, and security risks has increased the demand for improved detection and classification systems. This state-of-the-art review presents a detailed overview of current improvements in drone detection and classification techniques: highlighting novel strategies used to address the rising concerns about UAV activities. We investigate the threats and challenges faced due to drones' dynamic behavior, size and speed diversity, battery life, etc. Furthermore, we categorize the key detection modalities, including radar, radio frequency (RF), acoustic, and vision-based approaches, and examine their distinct advantages and limitations. The research also discusses the importance of sensor fusion methods and other detection approaches, including wireless fidelity (Wi-Fi), cellular, and Internet of Things (IoT) networks, for improving the accuracy and efficiency of UAV detection and identification.
PubMed: 38202987
DOI: 10.3390/s24010125 -
Patterns (New York, N.Y.) Sep 2022In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used...
In this study, we analyzed health-advertising tactics of digital medicine companies (n = 5) to evaluate varying types of cross-site-tracking middleware (n = 32) used to extract health information from users. More specifically, we examine how browsing data can be exchanged between digital medicine companies and Facebook for advertising and lead generation and advertising purposes. Our analysis focused on companies offering services to patient advocates in the cancer community who frequently engage on social media. We co-produced this study with public cancer advocates leading or participating in breast cancer groups on Facebook. Following our analysis, we raise policy questions about what constitutes a health privacy breach based on existing federal laws such as the Health Breach Notification Rule and The HIPAA Privacy Rule. We discuss how these common marketing practices enable surveillance and targeting of medical ads to vulnerable patient populations without consent.
PubMed: 36124307
DOI: 10.1016/j.patter.2022.100561 -
NPJ Digital Medicine 2020The lack of interoperability in Britain's medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing... (Review)
Review
The lack of interoperability in Britain's medical records systems precludes the realisation of benefits generated by increased spending elsewhere in healthcare. Growing concerns regarding the security of online medical data following breaches, and regarding regulations governing data ownership, mandate strict parameters in the development of efficient methods to administrate medical records. Furthermore, consideration must be placed on the rise of connected devices, which vastly increase the amount of data that can be collected in order to improve a patient's long-term health outcomes. Increasing numbers of healthcare systems are developing Blockchain-based systems to manage medical data. A Blockchain is a decentralised, continuously growing online ledger of records, validated by members of the network. Traditionally used to manage cryptocurrency records, distributed ledger technology can be applied to various aspects of healthcare. In this manuscript, we focus on how Electronic Medical Records in particular can be managed by Blockchain, and how the introduction of this novel technology can create a more efficient and interoperable infrastructure to manage records that leads to improved healthcare outcomes, while maintaining patient data ownership and without compromising privacy or security of sensitive data.
PubMed: 31934645
DOI: 10.1038/s41746-019-0211-0 -
Neurologic Clinics Aug 2023Advances in electronic health record technology, the ever-expanding use of social media, and cybersecurity sabotage threaten patient privacy and render physicians and... (Review)
Review
Advances in electronic health record technology, the ever-expanding use of social media, and cybersecurity sabotage threaten patient privacy and render physicians and health care organizations liable for violating federal and state laws. Violating a patient's privacy is both an ethical and legal breach with potentially serious legal and reputational consequences. Even an unintentional Health Insurance Portability and Accountability Act of 1996 (HIPAA) violation can result in financial penalties and reputational harm. Staying complaint with HIPAA requires vigilance on the part of both individuals with legitimate access to protected health information (PHI) and the organizations handling that PHI.
Topics: United States; Humans; Health Insurance Portability and Accountability Act; Privacy; Social Media; Confidentiality
PubMed: 37407103
DOI: 10.1016/j.ncl.2023.03.007 -
IEEE Journal of Biomedical and Health... Dec 2022Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as...
Federated learning methods offer secured monitor services and privacy-preserving paradigms to end-users and organisations in the Internet of Things networks such as smart healthcare systems. Federated learning has been coined to safeguard sensitive data, and its global aggregation is often based on a centralised server. This design is vulnerable to malicious attacks and could be breached by privacy attacks such as inference and free-riding, leading to inefficient training models. Besides, uploaded analysing parameters by patients can reveal private information and the threat of direct manipulation by the central server. To address these issues, we present a three-fold Federated Edge Aggregator, the so-called Edge Intelligence, a federated learning-based privacy protection framework for safeguarding Smart Healthcare Systems at the edge against such privacy attacks. We employ an iteration-based Conventional Neural Network (CNN) model and artificial noise functions to balance privacy protection and model performance. A theoretical convergence bound of Edge Intelligence on the trained federated learning model's loss function is also introduced here. We evaluate and compare the proposed framework with the recently established methods using model performance and privacy budget on popular and recent datasets: MNIST, CIFAR10, STL10, and COVID19 chest x-ray. Finally, the proposed framework achieves 90% accuracy and a high privacy rate demonstrating better performance than the baseline technique.
Topics: Humans; Privacy; COVID-19; Fenbendazole; Intelligence; Internet
PubMed: 35857737
DOI: 10.1109/JBHI.2022.3192648 -
Current Opinion in Psychology Dec 2020Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on... (Review)
Review
Mental healthcare providers increasingly use technology for psychotherapy services. This progress enables professionals to communicate, store information, and rely on digital software and hardware. Emails, text messaging, telepsychology/telemental health therapy, electronic medical records, cloud-based storage, apps/applications, and assessments are now available within the provision of services. Of those mentioned, some are directly utilized for psychotherapy while others indirectly aid providers. Whereas professionals previously wrote notes locally, technology has empowered providers to work more efficiently with third-party services and solutions. However, the implementation of these advancements in mental healthcare involves consequences to digital privacy and might increase clients' risk of unintended breaches of confidentiality. This manuscript reviews common technologies, considers the vulnerabilities therein, and proposes suggestions to strengthen privacy.
Topics: Confidentiality; Electronic Health Records; Humans; Mental Health Services; Privacy; Technology
PubMed: 32361651
DOI: 10.1016/j.copsyc.2020.03.012 -
International Journal of Environmental... Aug 2023Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the... (Review)
Review
Federated learning (FL) provides a distributed machine learning system that enables participants to train using local data to create a shared model by eliminating the requirement of data sharing. In healthcare systems, FL allows Medical Internet of Things (MIoT) devices and electronic health records (EHRs) to be trained locally without sending patients data to the central server. This allows healthcare decisions and diagnoses based on datasets from all participants, as well as streamlining other healthcare processes. In terms of user data privacy, this technology allows collaborative training without the need of sharing the local data with the central server. However, there are privacy challenges in FL arising from the fact that the model updates are shared between the client and the server which can be used for re-generating the client's data, breaching privacy requirements of applications in domains like healthcare. In this paper, we have conducted a review of the literature to analyse the existing privacy and security enhancement methods proposed for FL in healthcare systems. It has been identified that the research in the domain focuses on seven techniques: Differential Privacy, Homomorphic Encryption, Blockchain, Hierarchical Approaches, Peer to Peer Sharing, Intelligence on the Edge Device, and Mixed, Hybrid and Miscellaneous Approaches. The strengths, limitations, and trade-offs of each technique were discussed, and the possible future for these seven privacy enhancement techniques for healthcare FL systems was identified.
Topics: Humans; Privacy; Blockchain; Computer Communication Networks; Electronic Health Records; Delivery of Health Care
PubMed: 37569079
DOI: 10.3390/ijerph20156539 -
BMJ Health & Care Informatics Dec 2021Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers... (Review)
Review
OBJECTIVES
Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare.
METHODS
We undertook an exploratory analysis of articles whose titles or abstracts contained the terms 'artificial intelligence' or 'AI' and 'medical' or 'healthcare' and 'attitudes', 'perceptions', 'opinions', 'views', 'expectations'. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making.
RESULTS
Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician-patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty.
DISCUSSION
Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined.
CONCLUSION
Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
Topics: Artificial Intelligence; Attitude; Delivery of Health Care; Humans; Names
PubMed: 34887331
DOI: 10.1136/bmjhci-2021-100450