scholarly journals A Privacy Risk Assessment Model for Medical Big Data Based on Adaptive Neuro-Fuzzy Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Mingyue Shi ◽  
Rong Jiang ◽  
Wei Zhou ◽  
Sen Liu ◽  
Savio Sciancalepore
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Privacy Protection ◽  
Internet Security ◽  
Assessment Model ◽  
Security And Privacy ◽  
Risk Assessment Model ◽  
Experimental Process ◽  
Data Environment ◽  
Medical Big Data

Information leakage in the medical industry has become an urgent problem to be solved in the field of Internet security. However, due to the need for automated or semiautomated authorization management for privacy protection in the big data environment, the traditional privacy protection model cannot adapt to this complex open environment. Although some scholars have studied the risk assessment model of privacy disclosure in the medical big data environment, it is still in the initial stage of exploration. This paper analyzes the key indicators that affect medical big data security and privacy leakage, including user access behavior and trust, from the perspective of users through literature review and expert consultation. Also, based on the user’s historical access information and interaction records, the user’s access behavior and trust are quantified with the help of information entropy and probability, and a definition expression is given explicitly. Finally, the entire experimental process and specific operations are introduced in three aspects: the experimental environment, the experimental data, and the experimental process, and then, the predicted results of the model are compared with the actual output through the 10-fold cross verification with Matlab. The results prove that the model in this paper is feasible. In addition, the method in this paper is compared with the current more classical medical big data risk assessment model, and the results show that when the proportion of illegal users is less than 15%, the model in this paper is more superior in terms of accuracy and recall.

Using big data to retrospectively validate the COMPASS-CAT risk assessment model: considerations on methodology

2020 ◽  
Author(s):  
Ilias Nikolakopoulos ◽  
Soheila Nourabadi ◽  
Joanna B. Eldredge ◽  
Lalitha Anand ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Assessment Model ◽  
Risk Assessment Model

scholarly journals CSCCRA: A Novel Quantitative Risk Assessment Model for SaaS Cloud Service Providers

Computers ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 66
Author(s):  
Olusola Akinrolabu ◽  
Steve New ◽  
Andrew Martin
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Service Providers ◽  
Cloud Service ◽  
Assessment Model ◽  
Quantitative Risk Assessment ◽  
Security And Privacy ◽  
Risk Assessment Model ◽  
Security Risks ◽  
Cloud Service Providers

Security and privacy concerns represent a significant hindrance to the widespread adoption of cloud computing services. While cloud adoption mitigates some of the existing information technology (IT) risks, research shows that it introduces a new set of security risks linked to multi-tenancy, supply chain and system complexity. Assessing and managing cloud risks can be a challenge, even for cloud service providers (CSPs), due to the increased numbers of parties, devices and applications involved in cloud service delivery. The limited visibility of security controls down the supply chain, further exacerbates this risk assessment challenge. As such, we propose the Cloud Supply Chain Cyber Risk Assessment (CSCCRA) model, a quantitative risk assessment model which is supported by supplier security posture assessment and supply chain mapping. Using the CSCCRA model, we assess the risk of a SaaS application, mapping its supply chain, identifying weak links in the chain, evaluating its security risks and presenting the risk value in monetary terms (£), with this, promoting cost-effective risk mitigation and optimal risk prioritisation. We later apply the Core Unified Risk Framework (CURF) in comparing the CSCCRA model with already established methods, as part of evaluating its completeness.

Financial risk assessment model based on big data

2019 ◽  
Vol 10 (04) ◽  
pp. 1950021 ◽  
Author(s):  
Qiong Kang
Keyword(s):  
Risk Assessment ◽  
Big Data ◽  
Regression Analysis ◽  
Data Analysis ◽  
Financial Risk ◽  
Big Data Analysis ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Analysis Method ◽  
Variable Model

Conventional financial risk assessment is not accurate and its adaptive assessment ability is low. In order to solve this problem, a financial risk assessment model based on big data is proposed. In this method, the quantitative analysis method is adopted to analyze the explanatory variable model and the control variable model of financial risk assessment. The market-to-book ratio, asset–liability ratio, cash flow ratio and financing structure model are adopted as constraint parameters to construct a big data analysis model for financial risk assessment. On this basis, the adaptive fuzzy weighted control method is adopted for information fusion of financial risk assessment data and big data classification, and the asset income control and innovative evaluation model are adopted for linear planning and square fitting during financial risk assessment. Based on the intervention factors of financial market participants, quantitative regression analysis is performed, and according to the economic game theory, big data analysis and prediction of financial risk assessment are performed through the regression analysis method. Then the big data fusion and clustering algorithms are adopted for financial risk assessment. The simulation results show that this method can provide a relatively high accuracy in financial risk assessment, and has relatively strong adaptive evaluation capability to the risk coefficient, so it has a good application value in the prevention and control of risk factors in financial systems.

Risk assessment model for venous thromboembolism in hospitalized surgical and non-surgical patients in the 4th Hungarian Antithrombotic Guidelines entitled “Diminution and Treatment of Venous Thromboembolism”

2010 ◽  
Vol 151 (34) ◽  
pp. 1365-1374 ◽  
Author(s):  
Marianna Dávid ◽  
Hajna Losonczy ◽  
Miklós Udvardy ◽  
Zoltán Boda ◽  
György Blaskó ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Assessment Model ◽  
Surgical Patients ◽  
Risk Assessment Model

A kórházban kezelt sebészeti és belgyógyászati betegekben jelentős a vénásthromboembolia-rizikó. Profilaxis nélkül, a műtét típusától függően, a sebészeti beavatkozások kapcsán a betegek 15–60%-ában alakul ki mélyvénás trombózis vagy tüdőembólia, és az utóbbi ma is vezető kórházi halálok. Bár a vénás thromboemboliát leggyakrabban a közelmúltban végzett műtéttel vagy traumával hozzák kapcsolatba, a szimptómás thromboemboliás események 50–70%-a és a fatális tüdőembóliák 70–80%-a nem a sebészeti betegekben alakul ki. Nemzetközi és hazai felmérések alapján a nagy kockázattal rendelkező sebészeti betegek többsége megkapja a szükséges trombózisprofilaxist. Azonban profilaxis nélkül marad a rizikóval rendelkező belgyógyászati betegek jelentős része, a konszenzuson alapuló nemzetközi és hazai irányelvi ajánlások ellenére. A belgyógyászati betegek körében növelni kell a profilaxisban részesülők arányát és el kell érni, hogy trombózisrizikó esetén a betegek megkapják a hatásos megelőzést. A beteg trombóziskockázatának felmérése fontos eszköze a vénás thromboembolia által veszélyeztetett betegek felderítésének, megkönnyíti a döntést a profilaxis elrendeléséről és javítja az irányelvi ajánlások betartását. A trombózisveszély megállapításakor, ha nem ellenjavallt, profilaxist kell alkalmazni. „A thromboemboliák kockázatának csökkentése és kezelése” című, 4. magyar antithromboticus irányelv felhívja a figyelmet a vénástrombózis-rizikó felmérésének szükségességére, és elsőként tartalmazza a kórházban fekvő belgyógyászati és sebészeti betegek kockázati kérdőívét. Ismertetjük a kockázatbecslő kérdőíveket és áttekintjük a kérdőívekben szereplő rizikófaktorokra vonatkozó bizonyítékokon alapuló adatokat.

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