Selected Cloud Security Patterns to Improve End User Security and Privacy in Public Clouds

2016 ◽  
pp. 115-132 ◽  
Author(s):  
Thomas Länger ◽  
Henrich C. Pöhls ◽  
Solange Ghernaouti
Keyword(s):  
Cloud Security ◽  
Security And Privacy ◽  
End User ◽  
Security Patterns ◽  
User Security

scholarly journals Metamodel for Security and Privacy Knowledge in Cloud Service Development

2019 ◽  
Author(s):  
Tian Xia ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa ◽  
Takehisa Kato ◽  
Haruhiko Kaiya ◽  
...  
Keyword(s):  
Case Studies ◽  
Cloud Service ◽  
Cloud Security ◽  
Cloud Services ◽  
Security And Privacy ◽  
Service Development ◽  
Security Patterns ◽  
Cloud Systems ◽  
Specific Scenario ◽  
Consistent Approach

Requirements for cloud services include security and privacy. Although many security patterns, privacy patterns, and non-pattern-based knowledge have been reported, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the Cloud Security and Privacy Metamodel (CSPM). CSPM uses a consistent approach to classify and support existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

Factors in an end user security expertise instrument

2017 ◽  
Vol 25 (2) ◽  
pp. 190-205 ◽  
Author(s):  
Prashanth Rajivan ◽  
Pablo Moriano ◽  
Timothy Kelley ◽  
L. Jean Camp
Keyword(s):  
Computer Security ◽  
Computer Skills ◽  
End Users ◽  
Security And Privacy ◽  
End User ◽  
Content Type ◽  
Logistic Regression Models ◽  
Wide Range ◽  
User Comprehension ◽  
User Security

Purpose The purpose of this study is to identify factors that determine computer and security expertise in end users. They can be significant determinants of human behaviour and interactions in the security and privacy context. Standardized, externally valid instruments for measuring end-user security expertise are non-existent. Design/methodology/approach A questionnaire encompassing skills and knowledge-based questions was developed to identify critical factors that constitute expertise in end users. Exploratory factor analysis was applied on the results from 898 participants from a wide range of populations. Cluster analysis was applied to characterize the relationship between computer and security expertise. Ordered logistic regression models were applied to measure efficacy of the proposed security and computing factors in predicting user comprehension of security concepts: phishing and certificates. Findings There are levels to peoples’ computer and security expertise that could be reasonably measured and operationalized. Four factors that constitute computer security-related skills and knowledge are, namely, basic computer skills, advanced computer skills, security knowledge and advanced security skills, and these are identified as determinants of computer expertise. Practical implications Findings from this work can be used to guide the design of security interfaces such that it caters to people with different expertise levels and does not force users to exercise more cognitive processes than required. Originality/value This work identified four factors that constitute security expertise in end users. Findings from this work were integrated to propose a framework called Security SRK for guiding further research on security expertise. This work posits that security expertise instrument for end user should measure three cognitive dimensions: security skills, rules and knowledge.

scholarly journals CSPM

2021 ◽  
Vol 12 (2) ◽  
pp. 1-18
Author(s):  
Tian Xia ◽  
Hironori Washizaki ◽  
Yoshiaki Fukazawa ◽  
Haruhiko Kaiya ◽  
Shinpei Ogata ◽  
...  
Keyword(s):  
Case Studies ◽  
Cloud Security ◽  
Cloud Services ◽  
Security And Privacy ◽  
Security Patterns ◽  
Privacy Concerns ◽  
Cloud Systems ◽  
Specific Scenario ◽  
Consistent Approach

Security and privacy in cloud systems are critical. To address security and privacy concerns, many security patterns, privacy patterns, and non-pattern-based knowledge have been reported. However, knowing which pattern or combination of patterns to use in a specific scenario is challenging due to the sheer volume of options and the layered cloud stack. To deal with security and privacy in cloud services, this study proposes the cloud security and privacy metamodel (CSPM). CSPM uses a consistent approach to classify and handle existing security and privacy patterns. In addition, CSPM is used to develop a security and privacy awareness process to develop cloud systems. The effectiveness and practicality of CSPM is demonstrated via several case studies.

SECURITY IN CLOUD COMPUTING IN INDIAN GOVERNMENT

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Nitin Vishnu Choudhari ◽  
Dr. Ashish B Sasankar
Keyword(s):  
Cloud Computing ◽  
Service Delivery ◽  
Service Providers ◽  
Cloud Service ◽  
Cloud Security ◽  
End Users ◽  
Security Architecture ◽  
Security Measures ◽  
End User ◽  
Cloud Service Providers

Abstract –Today Security issue is the topmost problem in the cloud computing environment. It leads to serious discomfort to the Governance and end-users. Numerous security solutions and policies are available however practically ineffective in use. Most of the security solutions are centered towards cloud technology and cloud service providers only and no consideration has been given to the Network, accessing, and device securities at the end-user level. The discomfort at the end-user level was left untreated. The security of the various public, private networks, variety of devices used by end-users, accessibility, and capacity of end-users is left untreated. This leads towards the strong need for the possible modification of the security architecture for data security at all levels and secured service delivery. This leads towards the strong need for the possible adaption of modified security measures and provisions, which shall provide secured hosting and service delivery at all levels and reduce the security gap between the cloud service providers and end-users. This paper investigates the study and analyze the security architecture in the Cloud environment of Govt. of India and suggest the modifications in the security architecture as per the changing scenario and to fulfill the future needs for the secured service delivery from central up to the end-user level. Keywords: Cloud Security, Security in GI Cloud, Cloud Security measures, Security Assessment in GI Cloud, Proposed Security for GI cloud

Updates in deep learning research in ophthalmology

2021 ◽  
Vol 135 (20) ◽  
pp. 2357-2376
Author(s):  
Wei Yan Ng ◽  
Shihao Zhang ◽  
Zhaoran Wang ◽  
Charles Jit Teng Ong ◽  
Dinesh V. Gunasekeran ◽  
...  
Keyword(s):  
Deep Learning ◽  
Critical Role ◽  
Disease Classification ◽  
Age Related Macular Degeneration ◽  
Security And Privacy ◽  
Generative Adversarial Networks ◽  
End User ◽  
Ethical Assessment ◽  
Age Related ◽  
Patient Consent

Abstract Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.

Review on Cloud Computing and Cloud Security Issues

2021 ◽  
Author(s):  
Ramla Humayun
Keyword(s):  
Cloud Computing ◽  
Cloud Security ◽  
Security And Privacy ◽  
Security Issues ◽  
Privacy Issues

Review on Cloud-Computing and the security and privacy issues related with it.

Impact of Big Data on Security

2018 ◽  
pp. 326-350
Author(s):  
Kasarapu Ramani
Keyword(s):  
Big Data ◽  
Defense Mechanisms ◽  
Security And Privacy ◽  
End User ◽  
Sensitive Data ◽  
Insider Threats ◽  
Security Issues ◽  
Commercial Importance ◽  
Multi Level ◽  
Access Privileges

Big data has great commercial importance to major businesses, but security and privacy challenges are also daunting this storage, processing, and communication. Big data encapsulate organizations' most important and sensitive data with multi-level complex implementation. The challenge for any organization is securing access to the data while allowing end user to extract valuable insights. Unregulated access privileges to the big data leads to loss or theft of valuable and sensitive. Privilege escalation leads to insider threats. Also, the computing architecture of big data is not focusing on session recording; therefore, it is becoming a challenge to identify potential security issues and to take remedial and mitigation mechanisms. Therefore, various big data security issues and their defense mechanisms are discussed in this chapter.

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