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PPSecS: Privacy-Preserving Secure Big Data Storage in a Cloud Environment.

Authors :
Bouleghlimat, Imene
Boudouda, Souheila
Hacini, Salima
Source :
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Mar2024, Vol. 49 Issue 3, p3225-3239. 15p.
Publication Year :
2024

Abstract

The proliferation of social networks, the Internet of Things, and economic mobility has led to an exponential increase in data. New data having high volume, high velocity, high variety, and high value are called big data. Big data present additional requirements in terms of storage and computation resources. Various enterprises aim to outsource their big data services to the cloud because of its cost efficiency, less management, resource pooling, and resilient computing. However, outsourcing the storage of sensitive data can expose them to potential security risks. Encryption presents a straightforward solution for data privacy preserving. In traditional encryption mechanisms, such as advanced encryption standard, the data owner and users must share an exact key for both data encryption and decryption. Currently, these mechanisms do not provide a scalable and secure solution for big data storage and analysis. Furthermore, they need to be more efficient to support big data velocity. Unfortunately, securing outsourced big data storage to a public cloud environment to later maintain efficient and secure processing over encrypted data by cloud servers cannot be ensured using traditional encryption mechanisms. In this paper, we propose a security approach for this issue by which honest but curious users or cloud service providers cannot reach complete information from the stored data. From the analysis, the proposed approach can provide secure cloud-assisted big data. Meanwhile, the performance evaluation shows the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
2193567X
Volume :
49
Issue :
3
Database :
Academic Search Index
Journal :
Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. )
Publication Type :
Academic Journal
Accession number :
175846425
Full Text :
https://doi.org/10.1007/s13369-023-07924-4