Security Pattern for Cloud SaaS: From System and Data Security to Privacy Case Study in AWS and Azure

Rath, Annanda Th.; Spasic, Bojan; Boucart, Nick; Thiran, Philippe

doi:10.3390/computers8020034

Cited by 22 publications

(12 citation statements)

References 14 publications

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“…It is not uncommon that the SaaS provider relies on platform services from another cloud provider, who in turn uses infrastructure services provisioned by yet another one. Since cloud applications are delivered through the Internet, they are also exposed to various external security risks, such as denial-of-service (DoS) or distributed denial-of-service (DDoS) attacks [29]. Therefore, we hypothesize that perceived IT security risk related to the use of the SaaS model has an impact on the perceived level of risk associated with the Software-as-a-Service model.…”

Section: Security Risksmentioning

confidence: 99%

Examining the Role of Trust and Risk in the Software-as-a-Service Adoption Decision

Kuciapski

Lustofin²,

Soja

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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The current study investigates the role of risk and trust in the Software-as-a-Service (SaaS) adoption decision. Drawing from the Web-Trust Model and Institutional Isomorphism Theory, the study proposes a multi-layered model explaining antecedents of the propensity to adopt SaaS. The model is next verified drawing from the opinions of 154 Polish IT practitioners with the Partial Least Squares (PLS) research approach. The results obtained in PLS analysis illustrate that both risk and trust directly influence the decision to adopt SaaS. However, the role of trust turned out more pronounced as trust also influences the decision to adopt SaaS indirectly through risk and, unlike risk, is also influenced by professional influence and external pressure. The findings also reveal varied importance of different types of risk for the SaaS adoption decision, highlight the paramount importance of operational risk and low significance of economic and legal risk.

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Section: Security Risksmentioning

confidence: 99%

Examining the Role of Trust and Risk in the Software-as-a-Service Adoption Decision

Kuciapski

Lustofin²,

Soja

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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“…Subburayalu et al 12 demonstrated the network performance will be degraded with the increased number of unauthenticated activities. For this purpose, there are different types of cryptography 11,[13][14][15][16] based security models are developed in the conventional works. These techniques help to protect the cloud data by performing the security operations: Most of the cloud security frameworks 17,18 perform these operations for increasing the privacy and confidentiality of the secret data.…”

Section: Introductionmentioning

confidence: 99%

An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

Selvam

Manimozhi

Ramachandran

2022

Concurrency and Computation

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Ensuring the reliable and secure data transmission in cloud systems is very essential and demanding task in the recent decades. For that purpose, various security frameworks have been deployed in the existing works, which are mainly focusing the improving the privacy and confidentiality of cloud data against the unauthenticated users. Yet, it facing the major problems of inefficient accessing, increased time consumption, computational complexity, storage complexity, and memory utilization rate.Hence, this research work intends to develop an advanced and efficient cryptography model, named as, hash based quadraplet wavelet permuted cryptography (HQWPC) for the secured cloud data storage and retrieval operations. Here, the bilinear mapping and group hash function generation processes are performed to generate the keys used for cryptographic operations. Also, the zig-zag scanning, wavelet coefficients extraction, and permutation processes are accomplished for data encryption. Consequently, the inverse of these operations is performed while decrypting the data. In addition to that, an integrated signature control policy authentication mechanism is employed for validating the authenticity of the cloud data users. This kind of signature verification process could efficiently increase the security level of cloud data. For validating the performance of the proposed security framework, various evaluation metrics have been utilized during analysis. Then, the obtained results are compared with the recent state-of-the-art models for proving the efficiency of the proposed technique over the other techniques.

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“…The system accuracy, the computational speed, and cost are important design parameters, especially for those systems intended for large populations [ 3 ]. Recently, biometric recognition systems have posed new challenges related to personal data protection (e.g., GDPR), which is not often considered by conventional recognition methods [ 4 ]. If biometric data are captured or stolen, they may be replicated and misused.…”

Section: Introductionmentioning

confidence: 99%

Privacy-Constrained Biometric System for Non-Cooperative Users

Jahromi

Buch-Cardona

Avots

et al. 2019

Entropy

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With the consolidation of the new data protection regulation paradigm for each individual within the European Union (EU), major biometric technologies are now confronted with many concerns related to user privacy in biometric deployments. When individual biometrics are disclosed, the sensitive information about his/her personal data such as financial or health are at high risk of being misused or compromised. This issue can be escalated considerably over scenarios of non-cooperative users, such as elderly people residing in care homes, with their inability to interact conveniently and securely with the biometric system. The primary goal of this study is to design a novel database to investigate the problem of automatic people recognition under privacy constraints. To do so, the collected data-set contains the subject's hand and foot traits and excludes the face biometrics of individuals in order to protect their privacy. We carried out extensive simulations using different baseline methods, including deep learning. Simulation results show that, with the spatial features extracted from the subject sequence in both individual hand or foot videos, state-of-the-art deep models provide promising recognition performance.

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Security Pattern for Cloud SaaS: From System and Data Security to Privacy Case Study in AWS and Azure

Cited by 22 publications

References 14 publications

Examining the Role of Trust and Risk in the Software-as-a-Service Adoption Decision

Examining the Role of Trust and Risk in the Software-as-a-Service Adoption Decision

An efficient and secure data sharing scheme for cloud data using hash based quadraplet wavelet permuted cryptography approach

Privacy-Constrained Biometric System for Non-Cooperative Users

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