scholarly journals Secure Medical Data Transmission Model for IoT-Based Healthcare Systems

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 20596-20608 ◽  
Author(s):  
Mohamed Elhoseny ◽  
Gustavo Ramirez-Gonzalez ◽  
Osama M. Abu-Elnasr ◽  
Shihab A. Shawkat ◽  
N. Arunkumar ◽  
...  
Author(s):  
Naresh Sammeta ◽  
Latha Parthiban

Recent healthcare systems are defined as highly complex and expensive. But it can be decreased with enhanced electronic health records (EHR) management, using blockchain technology. The healthcare sector in today’s world needs to address two major issues, namely data ownership and data security. Therefore, blockchain technology is employed to access and distribute the EHRs. With this motivation, this paper presents novel data ownership and secure medical data transmission model using optimal multiple key-based homomorphic encryption (MHE) with Hyperledger blockchain (OMHE-HBC). The presented OMHE-HBC model enables the patients to access their own data, provide permission to hospital authorities, revoke permission from hospital authorities, and permit emergency contacts. The proposed model involves the MHE technique to securely transmit the data to the cloud and prevent unauthorized access to it. Besides, the optimal key generation process in the MHE technique takes place using a hosted cuckoo optimization (HCO) algorithm. In addition, the proposed model enables sharing of EHRs by the use of multi-channel HBC, which makes use of one blockchain to save patient visits and another one for the medical institutions in recoding links that point to EHRs stored in external systems. A complete set of experiments were carried out in order to validate the performance of the suggested model, and the results were analyzed under many aspects. A comprehensive comparison of results analysis reveals that the suggested model outperforms the other techniques.


Author(s):  
G. Saravanakumar ◽  
T.M. Devi ◽  
N. Karthikeyan ◽  
B. John Samuel

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Ashwin Belle ◽  
Raghuram Thiagarajan ◽  
S. M. Reza Soroushmehr ◽  
Fatemeh Navidi ◽  
Daniel A. Beard ◽  
...  

The rapidly expanding field of big data analytics has started to play a pivotal role in the evolution of healthcare practices and research. It has provided tools to accumulate, manage, analyze, and assimilate large volumes of disparate, structured, and unstructured data produced by current healthcare systems. Big data analytics has been recently applied towards aiding the process of care delivery and disease exploration. However, the adoption rate and research development in this space is still hindered by some fundamental problems inherent within the big data paradigm. In this paper, we discuss some of these major challenges with a focus on three upcoming and promising areas of medical research: image, signal, and genomics based analytics. Recent research which targets utilization of large volumes of medical data while combining multimodal data from disparate sources is discussed. Potential areas of research within this field which have the ability to provide meaningful impact on healthcare delivery are also examined.


Author(s):  
Alexandra Teslya ◽  
Thi Mui Pham ◽  
Noortje G. Godijk ◽  
Mirjam E. Kretzschmar ◽  
Martin C.J. Bootsma ◽  
...  

AbstractBackgroundWith new cases of COVID-19 surging around the world, many countries have to prepare for moving beyond the containment phase. Prediction of the effectiveness of non-case-based interventions for mitigating, delaying or preventing the epidemic is urgent, especially for countries affected by the ongoing seasonal influenza activity.MethodsWe developed a transmission model to evaluate the impact of self-imposed prevention measures (handwashing, mask-wearing, and social distancing) due to the spread of COVID-19 awareness and of short-term government-imposed social distancing on the peak number of diagnoses, attack rate and time until the peak number of diagnoses.FindingsFor fast awareness spread in the population, self-imposed measures can significantly reduce the attack rate, diminish and postpone the peak number of diagnoses. A large epidemic can be prevented if the efficacy of these measures exceeds 50%. For slow awareness spread, self-imposed measures reduce the peak number of diagnoses and attack rate but do not affect the timing of the peak. Early implementation of short-term government interventions can only delay the peak (by at most 7 months for a 3-month intervention).InterpretationHandwashing, mask-wearing and social distancing as a reaction to information dissemination about COVID-19 can be effective strategies to mitigate and delay the epidemic. We stress the importance of rapidly spreading awareness on the use of these self-imposed prevention measures in the population. Early-initiated short-term government-imposed social distancing can buy time for healthcare systems to prepare for an increasing COVID-19 burden.FundingThis research was funded by ZonMw project 91216062, One Health EJP H2020 project 773830, Aidsfonds project P-29704.Research in contextEvidence before this studyEvidence to date suggests that containment of SARS-CoV-2 using quarantine, travel restrictions, isolation of symptomatic cases, and contact tracing may need to be supplemented by other interventions. Given its rapid spread across the world and immense implications for public health, it is urgent to understand whether non-case-based interventions can mitigate, delay or even prevent a COVID-19 epidemic. One such strategy is a broader-scale contact rate reduction enforced by governments which was used during previous outbreaks, e.g., the 1918 influenza pandemic and the 2009 influenza A/H1N1 pandemic in Mexico. Alternatively, governments and media may stimulate self-imposed prevention measures (handwashing, mask-wearing, and social distancing) by generating awareness about COVID-19, especially when economic and societal consequences are taken into account. Both of these strategies may have a significant impact on the outbreak dynamics. Currently, there are no comparative studies that investigate their viability for controlling a COVID-19 epidemic.Added value of this studyUsing a transmission model parameterized with current best estimates of epidemiological parameters, we evaluated the impact of handwashing, mask-wearing, and social distancing due to COVID-19 awareness and of government-imposed social distancing on the peak number of diagnoses, attack rate, and time until the peak number of diagnoses. We show that a short-term (1-3 months) government intervention initiated early into the outbreak can only delay the peak number of diagnoses but neither alters its magnitude nor the attack rate. Our analyses also highlight the importance of spreading awareness about COVID-19 in the population, as the impact of self-imposed measures is strongly dependent on it. When awareness spreads fast, simple self-imposed measures such as handwashing are more effective than short-term government intervention. Self-imposed measures do not only diminish and postpone the peak number of diagnoses, but they can prevent a large epidemic altogether when their efficacy is sufficiently high (above 50%). Qualitatively, these results will aid public health professionals to compare and select interventions for designing effective outbreak control policies.Implications of all available evidenceOur results highlight that dissemination of evidence-based information about effective prevention measures (hand-washing, mask-wearing, and self-imposed social distancing) can be a key strategy for mitigating and postponing a COVID-19 epidemic. Government interventions (e.g., closing schools and prohibiting mass gatherings) implemented early into the epidemic and lasting for a short-time can only buy time for healthcare systems to prepare for an increasing COVID-19 burden.


Author(s):  
Priyan Malarvizhi KUMAR ◽  
Choong Seon Hong ◽  
Fatemeh Afghah ◽  
Gunasekaran Manogaran ◽  
Keping Yu ◽  
...  

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