A data partitioning scheme for secure data storage in cloud environments

Simple item page
dc.contributor.authorSYED MUHAMMAD ALI RAZA
dc.date.accessioned2025-10-28T15:23:51Z
dc.date.available2025-10-28T15:23:51Z
dc.date.issued2021
dc.description.abstractData is being produced at very fast rate due to incremental use of social media, web services, mobile devices, cyber-physical systems and Internet of Things (IoT). Storing such huge data on local storage is not feasible for all the users due to certain issues like cost, maintenance, backup etc. Cloud Computing (CC) provides unlimited resources at minimal cost. However, storing the data in remote location raises privacy threats to data. Although the cloud service provider (CSP) offers mechanism to secure the data from outside attacks but there is still a risk of inside attacks on the data. To prevent such attacks Wang at al.(T. Wang et al., 2018) proposed a three-layered architecture by using fog computing. Although the scheme ensured the confidentiality of the data but data recovery mechanism was not provided. Data modification at different places without the knowledge of the data owner is also an issue. To address such issues, we proposed a data-partitioning scheme with recovery mechanism for secure storage of data in the multi-cloud environment. Simulation results depicted that decoding and encoding speeds of the proposed solution is 40% and 17 % faster than the existing techniques, respectively. Similarly, data recovery mechanism takes 60% less processing time then the existing technique.
dc.identifier.urihttps://escholar.umt.edu.pk/handle/123456789/9651
dc.language.isoen
dc.publisherUMT, Lahore
dc.titleA data partitioning scheme for secure data storage in cloud environments
dc.typeThesis
Files
Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
A DATA PARTITIONING SCHEME FOR SECURE DATA.pdf
Size:
2.03 MB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
2021