Advanced Cloud Privacy Threat Modeling

Gholami, Ali; Laure, Erwin

doi:10.5121/csit.2016.60120

Cited by 10 publications

(9 citation statements)

References 17 publications

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“…Various type of threat modelling which were identified in this study include Attack Tree (AT), Attack Graph (AG),Attack Surface (AS), Practical Threat Analysis (PTA) , Threat Model Framework for Personal Network (PN), STRIDE, Threat modeling in pervasive computing paradigm (TMP), [6], [14], [26]- [29], [50] and LINDDUN (Likability, Identifiability, Non-Repudiation, Detectability, Disclosure of Information, Unawareness and Non-Compliance) [30] as defined below:…”

Section: Healthcare Related Information Security Threats Threat Mmentioning

confidence: 99%

“…In the area of privacy, the LINDDUN (Likability, Identifiability, Non-Repudiation, Detectability, Disclosure of Information, Unawareness and Non-Compliance) privacy threat modeling framework has recently gained attention in the privacy community [30]. LINDDUN provides systematic support to elicit and mitigate privacy threats.…”

Section: Healthcare Related Information Security Threats Threat Mmentioning

confidence: 99%

“…According to [2], [10]. Though, LINDDUN model fill in the gap on privacy requirement assessment, it's focus is generally narrowed to obtaining privacy requirement by mapping data flow diagrams of the system to its application scenarios and related threats [30], [50].…”

Section: Identifying Counter Measuresmentioning

confidence: 99%

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Comparative Analysis of Threat Modeling Methods for Cloud Computing towards Healthcare Security Practice

Yeng¹,

Wolthusen²,

Yang³

2020

IJACSA

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Healthcare organizations consist of unique activities including collaborating on patients care and emergency care. The sector also accumulates high sensitive multifaceted patients' data such as text reports, radiology images and pathological slides. The large volume of the data is often stored as Electronic Health Records (EHR) which must be frequently updated while ensuring higher percentage up-time for constant availability of patients' records. Healthcare as a critical infrastructure also needs highly skilled IT personnel, Information and Communication Technology (ICT) and infrastructure with regular maintenance culture. Fortunately, cloud computing can provide these necessary services at a lower cost. But with all thees enormous benefits of cloud computing, it is characterized with various information security issues which is not enticing to healthcare. Amid many threat modelling methods, which of them is suitable for identifying cloud related threats towards the adoption of cloud computing for healthcare? This paper compared threat modelling methods to determine their suitability for identifying and managing healthcare related threats in cloud computing. Threat modelling in pervasive computing (TMP) was identified to be suitable and can be combined with Attack Tree (AT), Attack Graph (AG) and Practical Threat Analysis (PTA) or STRIDE (spoofing, tampering, repudiation, information disclosure, denial of service and elevation of privilege). Also Attack Tree (AT) could be complemented with TMP, AG and STRIDE or PTA. Healthcare IT security professionals can hence rely on these methods in their security practices, to identify cloud related threats for healthcare. Essentially, privacy related threat modeling methods such as LINDDUN framework, need to be included in these synergy of cloud related threat modelling methods towards enhancing security and privacy for healthcare needs.

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Section: Healthcare Related Information Security Threats Threat Mmentioning

confidence: 99%

Section: Healthcare Related Information Security Threats Threat Mmentioning

confidence: 99%

See 1 more Smart Citation

Comparative Analysis of Threat Modeling Methods for Cloud Computing towards Healthcare Security Practice

Yeng¹,

Wolthusen²,

Yang³

2020

IJACSA

View full text Add to dashboard Cite

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“…A threat model is defined as a framework which details internal and external vulnerabilities, as well as objectives and countermeasures [22]. Threat models are utilized across various disciplines such as cloud computing [23], health records [24], and storage [25]. Threat model for a multi-modal system such as VRLE can provide a systematic analysis of possible threats and help identify any module that is highly vulnerable to attacks.…”

Section: A Threat Modelmentioning

confidence: 99%

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Gulhane

Vyas

Mitra

et al. 2019

2019 16th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Social Virtual Reality based Learning Environments (VRLEs) such as vSocial render instructional content in a threedimensional immersive computer experience for training youth with learning impediments. There are limited prior works that explored attack vulnerability in VR technology, and hence there is a need for systematic frameworks to quantify risks corresponding to security, privacy, and safety (SPS) threats. The SPS threats can adversely impact the educational user experience and hinder delivery of VRLE content. In this paper, we propose a novel risk assessment framework that utilizes attack trees to calculate a risk score for varied VRLE threats with rate and duration of threats as inputs. We compare the impact of a well-constructed attack tree with an adhoc attack tree to study the trade-offs between overheads in managing attack trees, and the cost of risk mitigation when vulnerabilities are identified. We use a vSocial VRLE testbed in a case study to showcase the effectiveness of our framework and demonstrate how a suitable attack tree formalism can result in a more safer, privacy-preserving and secure VRLE system.

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“…In [54], the design and implementation of a security framework for BiobankCloud, a platform that supports the secure storage and processing of genomic data in cloud computing environments, has been discussed. The proposed framework is built on the cloud privacy threat modeling approach [55], [56] which is used to define the privacy threat model for processing next-generation sequencing data according to the DPD [2].…”

Section: Privacy-preservation For Sensitive Data In Cloud Computingmentioning

confidence: 99%

Security and Privacy of Sensitive Data in Cloud Computing : A Survey of Recent Developments

Gholami

Laure

2015

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Advanced Cloud Privacy Threat Modeling

Abstract: ABSTRACT

Cited by 10 publications

References 17 publications

Comparative Analysis of Threat Modeling Methods for Cloud Computing towards Healthcare Security Practice

Comparative Analysis of Threat Modeling Methods for Cloud Computing towards Healthcare Security Practice

Security, Privacy and Safety Risk Assessment for Virtual Reality Learning Environment Applications

Security and Privacy of Sensitive Data in Cloud Computing : A Survey of Recent Developments

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