<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Security is important in cloud data storage while using the cloud services provided by the service provider in the cloud. Most of the research works have been designed for a secure cloud data storage. However, cloud users still have security issues with their outsourced data. In order to overcome such limitations, a Dynamic Bloom Filter Hashing based Cloud Data Storage (DBFH-CDS) Technique is proposed. The main goal of DBFH-CDS Technique is to improve confidentiality and security of data storage in a cloud environment. The proposed Technique is implemented using data fragmentation model and Bloom filter. The DBFH-CDS Technique uses data fragmentation model for fragmenting the large cloud datasets. After that, Bloom Filter is employed in DBFH-CDS Technique for storing the fragmented sensitive data along with higher security. The DBFH-CDS Technique ensures high data confidentiality and security for cloud data storage with the help of Bloom Filter. The performance of proposed DBFH-CDS Technique is measured in terms of Execution time and Data retrieval efficiency. The experimental results show that the DBFH-CDS Technique is able to improve the cloud data storage security with minimum space complexity as compared to state-of-the-art-works.</p></abstract>

Security is important in cloud data storage while using the cloud services provided by the service provider in the cloud. Most of the research works have been designed for a secure cloud data storage. However, cloud users still have security issues with their outsourced data. In order to overcome such limitations, a Dynamic Bloom Filter Hashing based Cloud Data Storage (DBFH-CDS) Technique is proposed. The main goal of DBFH-CDS Technique is to improve confidentiality and security of data storage in a cloud environment. The proposed Technique is implemented using data fragmentation model and Bloom filter. The DBFH-CDS Technique uses data fragmentation model for fragmenting the large cloud datasets. After that, Bloom Filter is employed in DBFH-CDS Technique for storing the fragmented sensitive data along with higher security. The DBFH-CDS Technique ensures high data confidentiality and security for cloud data storage with the help of Bloom Filter. The performance of proposed DBFH-CDS Technique is measured in terms of Execution time and Data retrieval efficiency. The experimental results show that the DBFH-CDS Technique is able to improve the cloud data storage security with minimum space complexity as compared to state-of-the-art-works.

Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage"}

Security is important in cloud data storage while using the cloud services provided by the service provider in the cloud. Most of the research works have been...

Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

Publié en ligne: 29 mars 2019

Pages: 53 - 72

Reçu: 11 août 2018

Accepté: 14 févr. 2019

DOI: https://doi.org/10.2478/cait-2019-0003

Mots clés
Cloud data storage, Cloud users, security, Confidentiality, fragmented table, unfragmented table, Bloom filter

© 2019 S. Jegadeeswari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Efficient Dynamic Bloom Filter Hashing Fragmentation for Cloud Data Storage

Publié en ligne: 29 mars 2019

Pages: 53 - 72

Reçu: 11 août 2018

Accepté: 14 févr. 2019

DOI: https://doi.org/10.2478/cait-2019-0003

Mots clésCloud data storage, Cloud users, security, Confidentiality, fragmented table, unfragmented table, Bloom filter

© 2019 S. Jegadeeswari et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Cloud data storage, Cloud users, security, Confidentiality, fragmented table, unfragmented table, Bloom filter