The data intactness of efficient audit service outsourcing for data integrity in clouds

2021 ◽  
Author(s):  
U. Veeresh ◽  
G. Krishna Lava Kumar ◽  
S. Asif
Keyword(s):  
Data Integrity ◽  
Service Outsourcing

Efficient audit service outsourcing for data integrity in clouds

2012 ◽  
Vol 85 (5) ◽  
pp. 1083-1095 ◽  
Author(s):  
Yan Zhu ◽  
Hongxin Hu ◽  
Gail-Joon Ahn ◽  
Stephen S. Yau
Keyword(s):  
Data Integrity ◽  
Service Outsourcing

Data Integrity Techniques in Cloud Computing: An Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 121 ◽  
Author(s):  
Neha Thakur ◽  
Aman Kumar Sharma
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Advantages And Disadvantages ◽  
Security Models ◽  
Cloud Storage Service ◽  
Storage Service ◽  
Cloud Users ◽  
It Enterprises

Cloud computing has been envisioned as the definite and concerning solution to the rising storage costs of IT Enterprises. There are many cloud computing initiatives from IT giants such as Google, Amazon, Microsoft, IBM. Integrity monitoring is essential in cloud storage for the same reasons that data integrity is critical for any data centre. Data integrity is defined as the accuracy and consistency of stored data, in absence of any alteration to the data between two updates of a file or record.  In order to ensure the integrity and availability of data in Cloud and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their advantages and disadvantages.

Privacy Preserving Data Integrity Verification in Cloud

2014 ◽  
Vol 1 (2) ◽  
pp. 25-31
Author(s):  
T. Subha ◽  
◽  
S. Jayashri ◽  
Keyword(s):  
Data Integrity ◽  
Privacy Preserving ◽  
Integrity Verification

Towards guaranteed data-integrity: A method of preventing Questionable Research Practices

2018 ◽  
Author(s):  
Dick Bierman ◽  
Jacob Jolij
Keyword(s):  
Real Time ◽  
Open Data ◽  
Data Integrity ◽  
Drop Out ◽  
Research Practices ◽  
Major Aspect ◽  
Questionable Research Practices ◽  
Blockchain Technology ◽  
Integrity System ◽  
Secure Server

We have tested the feasibility of a method to prevent the occurrence of so-called Questionable Research Practices (QRP). A part from embedded pre-registration the major aspect of the system is real-time uploading of data on a secure server. We outline the method, discuss the drop-out treatment and compare it to the Born-open data method, and report on our preliminary experiences. We also discuss the extension of the data-integrity system from secure server to use of blockchain technology.

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