Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/533004
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dc.coverage.spatialComputer Science
dc.date.accessioned2023-12-26T05:22:10Z-
dc.date.available2023-12-26T05:22:10Z-
dc.identifier.urihttp://hdl.handle.net/10603/533004-
dc.description.abstractThe rapid adoption of cloud storage by enterprises has revolutionized data management practices, yet it has additionally presented challenges related to data security, storage efficiency, and redundancy. This research proposes novel methodologies to address these challenges and enhance and re-establish accomplishments in data deduplication, guaranteeing secure and efficient data management inside the cloud storage climate. At the enterprise level, the Secure Convergent Key Management Strategy (SCKMS) is presented, empowering secure tasks like looking through encrypted data, sharing data inside the enterprise, and downloading data from the cloud while limiting confidence in the cloud service provider (CSP). SCKMS integrates a proof of recuperation protocol that checks the realness of data proprietorship and a proof of possession protocol that permits clients to approve the honesty of their data against possibly malevolent CSPs. These protocols guarantee robust security and efficient data management inside the cloud. newline
dc.format.extentXxiii, 148p.
dc.languageEnglish
dc.relation180 Nos.
dc.rightsuniversity
dc.titleAn Enhanced And Secured Data Deduplication Using Application Aware Local Global Source And Distributed Hash Table Based Load Balancing Approach For Optimized Cloud Storage Environment
dc.title.alternative-
dc.creator.researcherUsharani, S
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Theory and Methods
dc.subject.keywordEngineering and Technology
dc.description.noteBibliography p.149-169
dc.contributor.guideKungumaraj, K
dc.publisher.placeKodaikanal
dc.publisher.universityMother Teresa Womens University
dc.publisher.institutionDepartment of Computer Science
dc.date.registered2015
dc.date.completed2023
dc.date.awarded2023
dc.format.dimensionsA4
dc.format.accompanyingmaterialDVD
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Department of Computer Science

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01_title.pdfAttached File249.41 kBAdobe PDFView/Open
02_preliminary pages.pdf809.63 kBAdobe PDFView/Open
03_contents.pdf314.37 kBAdobe PDFView/Open
04_abstract.pdf147.51 kBAdobe PDFView/Open
05_chapter 1.pdf1.1 MBAdobe PDFView/Open
06_chapter 2.pdf569.97 kBAdobe PDFView/Open
07_chapter 3.pdf524.27 kBAdobe PDFView/Open
08_chapter 4.pdf790.43 kBAdobe PDFView/Open
09_chapter 5.pdf859.99 kBAdobe PDFView/Open
10_chapter 6.pdf550.16 kBAdobe PDFView/Open
11_chapter 7.pdf155.27 kBAdobe PDFView/Open
12_chapter 8.pdf147.68 kBAdobe PDFView/Open
80_recommendation.pdf273.06 kBAdobe PDFView/Open


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