An effective, secure and efficient tagging method for integrity protection of outsourced data in a public cloud storage.


Journal

PloS one
ISSN: 1932-6203
Titre abrégé: PLoS One
Pays: United States
ID NLM: 101285081

Informations de publication

Date de publication:
2020
Historique:
received: 22 05 2020
accepted: 09 10 2020
entrez: 5 11 2020
pubmed: 6 11 2020
medline: 23 12 2020
Statut: epublish

Résumé

Data Integrity Auditing (DIA) is a security service for checking the integrity of data stored in a PCS (Public Cloud Storage), a third-party based storage service. A DIA service is provided by using integrity tags (hereafter referred to tags). This paper proposes a novel tagging method, called Tagging of Outsourced Data (TOD), for generating and verifying tags of files. TOD has a number of unique properties: (i) it supports both public and private verifiability, and achieves this property with a low level of overhead at the user end, making it particularly attractive to mobile users with resource-constrained devices, (ii) it protects data confidentiality, supports dynamic tags and is resilient against tag forgery and tag tampering (i.e. by authorised insiders) at the same time in more secure and efficient, making the method more suited to the PCS environment, (iii) it supports tags deduplication, making it more efficient, particularly for the user who has many files with data redundancy. Comprehensive security analysis and performance evaluation have been conducted to demonstrate the efficacy and efficiency of the approach taken in the design.

Identifiants

pubmed: 33151960
doi: 10.1371/journal.pone.0241236
pii: PONE-D-20-15002
pmc: PMC7643952
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e0241236

Déclaration de conflit d'intérêts

The authors have declared that no competing interests exist.

Références

ScientificWorldJournal. 2014;2014:269357
pubmed: 25121114

Auteurs

Reem ALmarwani (R)

College of Computer Science and Engineering (CCSE), Taibah University, Medina, Saudi Arabia.
Information Management Research Group, The Department of Computer Science, The University of Manchester, Manchester, United Kingdom.

Ning Zhang (N)

Information Management Research Group, The Department of Computer Science, The University of Manchester, Manchester, United Kingdom.

James Garside (J)

Information Management Research Group, The Department of Computer Science, The University of Manchester, Manchester, United Kingdom.

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Classifications MeSH