Speedy Cloud-RSA homomorphic scheme for preserving data confidentiality in cloud computing

Makkaoui, Khalid El; Beni‐Hssane, Abderrahim; Ezzati, Abdellah

doi:10.1007/s12652-018-0844-x

Cited by 25 publications

(7 citation statements)

References 28 publications

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“…Various attribute-based access control models have been presented in the literature (e.g., ciphertext-policy attribute-based encryption [35], secure billing protocol over attribute-based encryption [36]) for preserving confidential information in cloud-supported CV applications. Along with an appropriate access control model, asymmetric or public-key cryptography is necessary for information exchange in cloudsupported CV applications [37]- [40].…”

Section: A Confidentialitymentioning

confidence: 99%

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

Salek

Khan

Rahman

et al. 2022

IEEE Internet Things J.

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In an internet-of-things (IoT) environment, cloud computing is emerging as a technologically feasible and economically viable solution for supporting real-time and nonreal-time connected vehicle (CV) applications due to its unlimited storage, enormous computing capabilities, and cost advantage, i.e., cloud computing costs less than owning such systems. However, maintaining cybersecurity is a major challenge in cloud-supported CV applications as it requires CVs and various transportation or non-transportation services to exchange data with the cloud via multiple wired and wireless communication networks, such as long-term evolution (LTE) and Wi-Fi. In this paper, we review the cybersecurity requirements of cloud-supported CV applications, such as confidentiality, integrity, availability, authentication, accountability, and privacy. Our review also identifies the associated cybersecurity challenges that might impact cloudsupported CV applications and corresponding solutions to these challenges. In addition, we present future research opportunities to prevent and mitigate cybersecurity issues in cloud computing for CV-related applications.

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Section: A Confidentialitymentioning

confidence: 99%

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

Salek

Khan

Rahman

et al. 2022

IEEE Internet Things J.

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“…The research of privacy-preserving outsourced data focuses on anonymization-based methods [12][13][14][15][16][17][18], cryptographicbased methods [19][20][21][22][23][24], hybrid methods [2,[25][26][27], and methods that seek to improve the data utility [26,28,29]. Some recent studies have demonstrated the privacy requirements of incremental datasets [30][31][32] and multiple sensitive attributes [33][34][35].…”

Section: Related Workmentioning

confidence: 99%

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Mansour

Siraj

Ghaleb

et al. 2021

Wireless Communications and Mobile Computing

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Cloud computing plays an essential role as a source for outsourcing data to perform mining operations or other data processing, especially for data owners who do not have sufficient resources or experience to execute data mining techniques. However, the privacy of outsourced data is a serious concern. Most data owners are using anonymization-based techniques to prevent identity and attribute disclosures to avoid privacy leakage before outsourced data for mining over the cloud. In addition, data collection and dissemination in a resource-limited network such as sensor cloud require efficient methods to reduce privacy leakage. The main issue that caused identity disclosure is quasi-identifier (QID) linking. But most researchers of anonymization methods ignore the identification of proper QIDs. This reduces the validity of the used anonymization methods and may thus lead to a failure of the anonymity process. This paper introduces a new quasi-identifier recognition algorithm that reduces identity disclosure which resulted from QID linking. The proposed algorithm is comprised of two main stages: (1) attribute classification (or QID recognition) and (2) QID dimension identification. The algorithm works based on the reidentification of risk rate for all attributes and the dimension of QIDs where it determines the proper QIDs and their suitable dimensions. The proposed algorithm was tested on a real dataset. The results demonstrated that the proposed algorithm significantly reduces privacy leakage and maintains the data utility compared to recent related algorithms.

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“…The CD Port model does not assist complex NoSQL data models, including the graph model. Makkaoui et al [20] suggested the Cloud-RSA's two variants: i) Rebalanced Cloud-RSA together with ii) MultiPower Cloud-RSA for enhancing the cloud data confidentiality. The variants were centred on the forms of suggested Cloud-RSA and changing exponents.…”

Section: Related Workmentioning

confidence: 99%

Confidentiality Preserving and Data Portability for Secure Cloud Computing using LCC-AP and SP-GA

2020

IJATCSE

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Cloud computing (CC) allows obtaining computing resources on-demand and also allows storing avalanche of data with a high fault tolerance level. Security is extremely vital when storing an avalanche of data on the cloud for resolving a number of issues. Though several existing works were there to store data on the cloud, still there are some problems that the data owner has to suffer, such as lack of confidentiality, time delay in storing, and also data being attacked because of insider troubles. Thus, this paper strives to strengthen the sensitive Data Confidentiality (DC) in private as well as public cloud storage by proposing a new data portability and access policy scheme to resolve these afore-mentioned problems. A novel algorithm termed Sandpiper Genetic Algorithm (SP-GA) is launched to maintain proper portability between the cloud-severs. Then, Length Caesar Cipher based Access Policy (LCC-AP) technique is utilized to achieve DC. The proposed techniques guarantee DC, support portability, and also provide secured sharing of files among users. Experimental outcomes show the potential efficiency of SP-GA and LCC-AP

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Speedy Cloud-RSA homomorphic scheme for preserving data confidentiality in cloud computing

Cited by 25 publications

References 28 publications

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

A Review on Cybersecurity of Cloud Computing for Supporting Connected Vehicle Applications

Quasi‐Identifier Recognition Algorithm for Privacy Preservation of Cloud Data Based on Risk Reidentification

Confidentiality Preserving and Data Portability for Secure Cloud Computing using LCC-AP and SP-GA

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