Attribute-Based Homomorphic Encryption based Integrity Auditing for Secure Outsourced Storage in Cloud

2018 ◽  
Vol 4 (8) ◽  
pp. 8
Author(s):  
Saloni Atre ◽  
Mayank Namdev
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Data Access ◽  
Cloud Services ◽  
Cloud Data ◽  
Integrity Checking ◽  
Data Integrity Checking ◽  
Data Files ◽  
Data Auditing

Cloud computing is an enormous area which shares huge amount of data over cloud services and it has been increasing with its on-demand technology. Since, with these versatile cloud services, when the delicate data stored within the cloud storage servers, there are some difficulties which has to be managed like its Security Issues, Data Privacy, Data Confidentiality, Data Sharing and its integrity over the cloud servers dynamically. Also, the authenticity and data access control should be maintained in this wide environment. Thus, Attribute based Encryption (ABE) is a significant version of cryptographic technique in the cloud computing environment. Data integrity, one of the most burning challenges in secure cloud storage. Data auditing protocols enable a verifier to efficiently check the integrity of the files without downloading the entire file from the cloud. In this paper cloud data integrity checking is performed by introducing attribute-based cloud data auditing where users can upload files to cloud through some set of attributes and specify auditor to check the integrity of data files. Existing protocols are mostly based on public key infrastructure or an exact identity, which lacks ?exibility of key management. In this research work Cloud data integrity checking is performed by introducing attribute-based cloud data auditing where users can upload files to cloud through some set of attributes and specify auditor to check the integrity of data files. Variable attributes are used to generate the private key and their performance is evaluated under variable attribute list.

scholarly journals A Survey on Cloud Computing Techniques for Data Integrity Checking

2019 ◽  
Vol 7 (6) ◽  
pp. 130-141
Author(s):  
Rahma Haroun
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Large Data ◽  
Data Integrity ◽  
Cloud Service ◽  
Security Risk ◽  
Memory Space ◽  
Cloud Data ◽  
Integrity Checking ◽  
Data Integrity Checking

Cloud computing is a term that used instead of internet to describe the infrastructure, software services and storage via internet. Large data centers are available in cloud for remotely store user data. The users have no data control privileges when the data transferred to the Cloud and they are not aware of any security risk. Data can be altered by unauthorized user, threats and dishonest server. Farther more, Data which are either unused for a long a time or takes large memory space can be deleted by cloud service provider. The main issue of cloud computing today is data integrity and how can be maintaining. There for, security challenges users are need to ensure that their data are integral by periodically Data integrity checking. Several integrity checking techniques have been proposed to ensure the data integrity in cloud storage. This paper provides a survey of various data integrity checking techniques for cloud data stored. Objective of this survey focusing on existing integrity check techniques for cloud data storage and  presenting their characteristics, benefits, functionality and limitations.

scholarly journals A Provably Secure Distant Data Integrity Checking with Homomorphic Hash Function in Secure Cloud Storage

2019 ◽  
Vol 9 (1) ◽  
pp. 4601-4605
Keyword(s):  
Data Storage ◽  
Hash Function ◽  
Cloud Storage ◽  
Data Integrity ◽  
Integrity Checking ◽  
Cloud Server ◽  
Data Integrity Checking ◽  
Constant Cost ◽  
Major Application ◽  
Data Files

The cloud storage with user facilities like great data storage quality, higher computing, scalable and flexible has been one of the major application of cloud computing. Large number of data holders is subcontracting the files to the cloud. The cloud server being a public domain is not very reliable thus the data holders are required to find a reliable and trustworthy way to check the ownership of the data files that they subcontract on the cloud server which is present in a remote location. To tackle this drawback number of Distant Data Integrity Checking (DDIC) protocols is in the literature but these protocols have liabilities with respect to the data dynamics and the efficiency. This work recommends a novel DDIC protocol built across homomorphic-hash-function. This system gives a resistance against number of attacks like replay, replaces and falsifications. This work introduces the Operation-record-table(ORT) which is an optimized table resulting in keeping the constant cost for supporting the data variations and keep tracks of blocking the file operations.

scholarly journals Security Analysis and Improvements on a Remote Integrity Checking Scheme for Regenerating-Coding-Based Distributed Storage

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Guangjun Liu ◽  
Wangmei Guo ◽  
Ximeng Liu ◽  
Jinbo Xiong
Keyword(s):  
Distributed Storage ◽  
Security Analysis ◽  
Data Integrity ◽  
Integrity Checking ◽  
Cloud Server ◽  
Data Integrity Checking ◽  
Checking Scheme ◽  
Data Auditing ◽  
Distributed Cloud ◽  
Remote Data

Enabling remote data integrity checking with failure recovery becomes exceedingly critical in distributed cloud systems. With the properties of a lower repair bandwidth while preserving fault tolerance, regenerating coding and network coding (NC) have received much attention in the coding-based storage field. Recently, an outstanding outsourced auditing scheme named NC-Audit was proposed for regenerating-coding-based distributed storage. The scheme claimed that it can effectively achieve lightweight privacy-preserving data verification remotely for these networked distributed systems. However, our algebraic analysis shows that NC-Audit can be easily broken due to a potential defect existing in its schematic design. That is, an adversarial cloud server can forge some illegal blocks to cheat the auditor with a high probability when the coding field is large. From the perspective of algebraic security, we propose a remote data integrity checking scheme RNC-Audit by resorting to hiding partial critical information to the server without compromising system performance. Our evaluation shows that the proposed scheme has significantly lower overhead compared to the state-of-the-art schemes for distributed remote data auditing.

Merkle Tree and Blockchain-Based Cloud Data Auditing

2020 ◽  
Vol 10 (3) ◽  
pp. 54-66
Author(s):  
Arun Prasad Mohan ◽  
Mohamed Asfak R. ◽  
Angelin Gladston
Keyword(s):  
Data Integrity ◽  
Service Provisioning ◽  
Merkle Tree ◽  
Cloud Data ◽  
Integrity Checking ◽  
Dataset Size ◽  
Data Auditing ◽  
Core Idea ◽  
Cloud Audit ◽  
Verification Techniques

Cloud computing is the fastest growing and most promising field in the service provisioning segment. It has become a challenging task to provide security in the cloud. The purpose of this article is to suggest a better and efficient integrity verification technique for data referred to as cloud audit. The deployment of cloud storage services has significant benefits in the management of data for users. However, this raises many security concerns, and one of them is data integrity. Though public verification techniques serve the purpose they are vulnerable to procrastinating auditors who may not perform verifications on time. In this article, a cloud data auditing system is proposed. The proposed cloud data auditing system integrates Merkle Tree-based Cloud audit and the blockchain-based audit recording system, thus the core idea is to record each verification result into a blockchain as a transaction. Utilizing the time-sensitive nature of blockchain, the verifications are time-stamped after the corresponding transaction is recorded into the blockchain, which enables users to check whether auditors have performed the verifications at the prescribed time. The proposed cloud data auditing system is experimentally validated. The investigations with varied dataset size revealed less time taken, on an average of 0.25 milliseconds with the use of Merkle Tree. Further results reveal consistency of the data integrity checking.

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