A Fog Computing Solution for Context-Based Privacy Leakage Detection for Android Healthcare Devices

Gu, Jun; Huang, Ruicong; Jiang, Li; Qiao, Gongzhe; Du, Xiaojiang; Guizani, Mohsen

doi:10.3390/s19051184

Cited by 13 publications

(12 citation statements)

References 38 publications

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“…RFID protocol framework based on fog computing and blockchain is used for medical big data collection and data privacy protection [19][20][21]. Gu et al [19] proposed a security and privacy protection solution for fog computing, which designs a framework for security and privacy protection using fog computing and a privacy leakage based on contextbased dynamic and static information to improve health and medicine infrastructure. Silva et al [20] proposed a medical records management architecture based on fog computing.…”

Section: Related Workmentioning

confidence: 99%

“…Step 4 (17) Based on the received HMAC, WISP can confirm the timeliness of the message and the equality of the session keys calculated on both sides (18) After verifying successfully, Nbr++, K2 � � K' ⊕ NW; (19) WISP records (K2, Seq1, Nbr) to awaken the IMD antenna (20) When IMD detects the request, collects (K1, Seq1), and employs them to exchange data securely with the programmer. contrary, if the private key is used to encrypt data, only the corresponding public key can be used for decryption.…”

Section: Physiological Signals Data Sharing Model Based Onmentioning

confidence: 99%

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[Retracted] Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

Chen

Hong

Geng

et al. 2020

Journal of Healthcare Engineering

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The proliferation of physiological signals acquisition and monitoring system, has led to an explosion in physiological signals data. Additionally, RFID systems, blockchain technologies, and the fog computing mechanisms have significantly increased the availability of physiological signal information through big data research. The driver for the development of hybrid systems is the continuing effort in making health-care services more efficient and sustainable. Implantable medical devices (IMD) are therapeutic devices that are surgically implanted into patients’ body to continuously monitor their physiological parameters. Patients treat cardiac arrhythmia due to IMD therapeutic and life-saving benefits. We focus on hybrid systems developed for patient physiological signals for collection, storage protection, and monitoring in critical care and clinical practice. In order to provide medical data privacy protection and medical decision support, the hybrid systems are presented, and RFID, blockchain, and big data technologies are used to analyse physiological signals.

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Section: Related Workmentioning

confidence: 99%

Section: Physiological Signals Data Sharing Model Based Onmentioning

confidence: 99%

[Retracted] Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

Chen

Hong

Geng

et al. 2020

Journal of Healthcare Engineering

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“…We found that the leakage and loss of personal information was the main reason that prevented people from participating both in the two groups. We think this result may be due to the severe information leakage, which has been a common phenomenon in the internet age [35]. Apart from this, impairment of medical right and bias of exam results were also their concerns in our research.…”

Section: Discussionmentioning

confidence: 69%

Biospecimen Donation in Biobank Construction: Aspects Affecting Donation and Publics’ Concern

Zhou

Zhang

et al. 2019

Preprint

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Background A biobank is a storage facility which stores sample mainly for biological or medical researches. Biospecimen collection is the kind of work essential in the building of biobank. However, we found the collection works had the difficulties in the proceeding because of the low anticipation rate. Methods We here conducted a questionnaire including several questions covered several aspects in order to find out people’s attitude towards biospecimen donation. The questionnaire had 20 questions mainly focused on overall publics’ participation rates, matters that influence their participation and major publics’ concerns in biospecimen donation. Results In our survey, 1477 from 2200 distributed questionnaires were responded, and electronic questionnaires showed the highest response rate of 49.9%. In all respondents, 936 showed willingness in participation, providing a percentage of 63.4%. We found that most respondents lack the knowledge of biospecimen donation and biobanking but still have a positive attitude towards biospecimen donation. Several factors, including family disease history( p <0.05), previous donation history ( p <0.01), the knowledge of biospecimen donation ( p <0.01), the knowledge of biospecimen donation conception ( p <0.01) were closely linked to donation willingness. Among those factors, family disease history, brief knowledge about biospecimen donation were independent factors affecting donation willingness. The reverse health effects and privacy leakage were major concerns among the majority of respondents. Primary reasons affected willingness, or unwillingness donation were their benefic to public interests and privacy concerns. In summary, this survey we mainly discussed factors affecting publics’ willingness and issues people concerned most in biospecimen donation of a biobank. Conclusions Most respondents hold positive attitudes towards biospecimen donation but lack relevant knowledges. Several factors influenced donation willingness probably caused by those deficiency knowledge. Even though faced this challenge, responders’ merits of altruistic behavior may contribute to the act of donation. In addition, information leakage and health impairment remained the domain factors prevented their participation. Further works are required to eliminate those undesirable elements restrained biospecimen donation by the well biobank knowledge popularization and detailed pre-donation information exchange .

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“…The real-time feedback class encompasses solutions that aim at providing quick responses to critical situations in healthcare environments. Several articles target the most variate set of applications in that scope: (i) critical event detection [61,88]; (ii) warning systems [89]; (iii) security issues [49,90]; and (iv) breath support systems [91]; In the first topic, in [88], the authors implemented a real-time signal processing algorithm for fall detection, which is able to deliver information to caregivers. These algorithms are executed at the network's border by fog servers, which collect and process all health information.…”

Section: Real-time Feedbackmentioning

confidence: 99%

Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications

Vilela

Rodrigues

Righi

et al. 2020

Sensors

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Fog computing is a distributed infrastructure where specific resources are managed at the network border using cloud computing principles and technologies. In contrast to traditional cloud computing, fog computing supports latency-sensitive applications with less energy consumption and a reduced amount of data traffic. A fog device is placed at the network border, allowing data collection and processing to be physically close to their end-users. This characteristic is essential for applications that can benefit from improved latency and response time. In particular, in the e-Health field, many solutions rely on real-time data to monitor environments, patients, and/or medical staff, aiming at improving processes and safety. Therefore, fog computing can play an important role in such environments, providing a low latency infrastructure. The main goal of the current research is to present fog computing strategies focused on electronic-Health (e-Health) applications. To the best of our knowledge, this article is the first to propose a review in the scope of applications and challenges of e-Health fog computing. We introduce some of the available e-Health solutions in the literature that focus on latency, security, privacy, energy efficiency, and resource management techniques. Additionally, we discuss communication protocols and technologies, detailing both in an architectural overview from the edge devices up to the cloud. Differently from traditional cloud computing, the fog concept demonstrates better performance in terms of time-sensitive requirements and network data traffic. Finally, based on the evaluation of the current technologies for e-Health, open research issues and challenges are identified, and further research directions are proposed.

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A Fog Computing Solution for Context-Based Privacy Leakage Detection for Android Healthcare Devices

Cited by 13 publications

References 38 publications

[Retracted] Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

[Retracted] Merging RFID and Blockchain Technologies to Accelerate Big Data Medical Research Based on Physiological Signals

Biospecimen Donation in Biobank Construction: Aspects Affecting Donation and Publics’ Concern

Looking at Fog Computing for E-Health through the Lens of Deployment Challenges and Applications

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