Towards Privacy-Preserving Medical Cloud Computing Using Homomorphic Encryption

Kocabaş, Övünç; Soyata, Tolga

doi:10.4018/978-1-5225-9863-3.ch005

Cited by 40 publications

(24 citation statements)

References 37 publications

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“…These issues hinder the usage of cloud storage. However, the cloud service has been developed to enhance better privacy and encryption [8]. Therefore, it can be hypothesized that Hypothesis 2: Privacy may positively affect the decision to adopt cloud computing.…”

Section: ) Cloud Securitymentioning

confidence: 99%

Drivers of Cloud Computing Adoption in Small Medium Enterprises of Indonesia Creative Industry

Gui

Fernando

Shaharudin

et al. 2021

JOIV : Int. J. Inform. Visualization

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Cloud computing is one of the enablers of Industrial Revolution 4.0 (IR.40). IR 4.0 is advantageous as it allows companies to increase performance and productivity. However, there are many enablers of IR 4.0, such as big data analytics, cloud computing, machine learning, and blockchain. However, the readiest to be used technology is cloud computing. While the advantages of cloud computing are well understood from the perspective of the literature and companies' point of view, the empirical evidence is still scarce. The research explores the drivers of cloud adoption between small and medium-sized enterprises (SMEs) in Indonesia. The study method is a quantitative method through e-survey data collection analyzed using IBM SPSS and Smart PLS software. The recognition of drivers will allow IT decision-makers to design the right platform for SMEs, increasing their company competitiveness. The findings revealed that cloud flexibility, perceived concern, privacy, relative advantage, perceived cost-benefit, quality of service, and top management support are among the top cloud adoption priorities that need to be improved in the creative industry to ensure the adoption of cloud computing more apparent. The study's contribution revealed that cloud computing is no longer at the infant stage in terms of adoption. Thus, the findings paved the way for scholars to undertake future research focusing on cloud computing implementation. Companies, on the other hand, can learn from this research by improving the adoption aspects.

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Section: ) Cloud Securitymentioning

confidence: 99%

Drivers of Cloud Computing Adoption in Small Medium Enterprises of Indonesia Creative Industry

Gui

Fernando

Shaharudin

et al. 2021

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…Different from the edge side, the cloud has enough computing power and storage capacity to meet the deployment and implementation of various security policies [7,8], so most of the computing and data storage are arranged in the cloud. However, the medical cloud platform requires faster response and higher timeliness.…”

Section: Proposed Platform Architecturementioning

confidence: 99%

Privacy Data Security Policy of Medical Cloud Platform Based on Lightweight Algorithm Model

Liu

Zhao

Liu³

et al. 2021

Scientific Programming

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The deterioration of aging population has seriously hindered the development of society. Medical cloud platform has been widely used to alleviate the pressure of aging population on social economy. Most of them collect the user’s sign information through the edge node and complete the disease prediction and diagnosis function combined with the cloud platform. However, the limited resources prevent the edge node from deploying the corresponding security policy after completing the data collection, storage, and calculation, which makes the edge data easy to be stolen. This paper proposes a security architecture of medical cloud platform based on lightweight algorithm model, which not only satisfies the needs of medical cloud platform to complete disease prediction and diagnosis accurately, but also creates a more secure edge node environment combined with other security strategies and hardware design. Finally, the prediction of cerebrovascular disease is used to verify the effectiveness of the proposed algorithm model.

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“…However, health data is highly sensitive and sharing or obtaining medical data is challenging due to privacy regulations such as HIPAA [114]. Cloud computing has great potential in the medical ield in applications such as personal health monitoring tools, but strict privacy regulations have slowed adoption of such technologies [137]. As fully homomorphic encryption becomes increasingly practical and standardized, its applications for artiicial intelligence in the medical domain have grown.…”

Section: Privacy-preserving Artificial Intelligence In Medicinementioning

confidence: 99%

“…The (encrypted) result is returned to the client, who then decrypts to privately view her result. Privacy-preserving machine learning has applications in personalized medicine [152,153], DNA sequence analysis [20], and diagnostic tools [137]. Possible models include:…”

Section: Privacy-preserving Artificial Intelligence In Medicinementioning

confidence: 99%

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

2020

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Machine learning and statistical techniques are powerful tools for analyzing large amounts of medical and genomic data. On the other hand, ethical concerns and privacy regulations prevent free sharing of this data. Encryption techniques such as fully homomorphic encryption (FHE) enable evaluation over encrypted data. Using FHE, machine learning models such as deep learning, decision trees, and naive Bayes have been implemented for privacy-preserving applications using medical data. These applications include classifying encrypted data and training models on encrypted data. FHE has also been shown to enable secure genomic algorithms, such as paternity and ancestry testing and privacy-preserving applications of genome-wide association studies. This survey provides an overview of fully homomorphic encryption and its applications in medicine and bioinformatics. The high-level concepts behind FHE and its history are introduced and details on current open-source implementations are provided. The state of fully homomorphic encryption for privacy-preserving techniques in machine learning and bioinformatics is reviewed, along with descriptions of how these methods can be implemented in the encrypted domain. CCS Concepts: • Security and privacy → Cryptography; Usability in security and privacy; • Social and professional topics → Medical information policy.

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Towards Privacy-Preserving Medical Cloud Computing Using Homomorphic Encryption

Cited by 40 publications

References 37 publications

Drivers of Cloud Computing Adoption in Small Medium Enterprises of Indonesia Creative Industry

Drivers of Cloud Computing Adoption in Small Medium Enterprises of Indonesia Creative Industry

Privacy Data Security Policy of Medical Cloud Platform Based on Lightweight Algorithm Model

Homomorphic Encryption for Machine Learning in Medicine and Bioinformatics

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