Secure Identity-Based Proxy Signature With Computational Diffie-Hellman for Cloud Data Management

2020 ◽  
pp. 79-106
Author(s):  
Dharavath Ramesh ◽  
Rahul Mishra ◽  
Damodar Reddy Edla ◽  
Madhu Sake
Keyword(s):  
Data Management ◽  
Proxy Signature ◽  
Cloud Data ◽  
Security Issues ◽  
Computing Services ◽  
Diffie Hellman ◽  
Cloud Framework ◽  
Identity Based ◽  
Heterogeneous Cloud ◽  
Cloud Data Management

This chapter explains a secure smart cloud framework based on identity-based proxy signature (IDBPS) scheme on Computational Diffie-Hellman (CD-H) assumption and AckIBE for data management. The objective of this chapter is to construct a secure hierarchical structure of homogeneous and heterogeneous cloud centers. This structure gives various types of computing services in the support of data analysis and information management. In this, the authors also introduce a security-related solution based on acknowledgment identity-based encryption (AckIBE), an IDBPS on computational Diffie-Hellman assumption, and identity-based proxy re-encryption to face critical security issues of the proposed framework.

Benchmarking and Analyzing the Energy Consumption of Cloud Data Management System

2014 ◽  
Vol 36 (7) ◽  
pp. 1485-1499 ◽  
Author(s):  
Jie SONG ◽  
Tian-Tian LI ◽  
Zhi-Liang ZHU ◽  
Yu-Bin BAO ◽  
Ge YU
Keyword(s):  
Energy Consumption ◽  
Data Management ◽  
Management System ◽  
Data Management System ◽  
Cloud Data ◽  
Cloud Data Management

Errata for "Unifying consensus and atomic commitment for effective cloud data management"

2021 ◽  
Vol 14 (7) ◽  
pp. 1166-1166
Author(s):  
Sujaya Maiyya ◽  
Faisal Nawab ◽  
Divyakant Agrawal ◽  
Amr El Abbadi
Keyword(s):  
Data Management ◽  
Cloud Data ◽  
Minor Error ◽  
Failure Scenario ◽  
A Minor ◽  
Cloud Data Management ◽  
Atomic Commitment

This errata article discusses and corrects a minor error in our work published in VLDB 2019. The discrepancy specifically pertains to Algorithms 3 and 4. The algorithms presented in the paper are biased towards a commit decision in a specific failure scenario. We explain the error using an example before correcting the algorithm.

scholarly journals An enhanced security tree to secure cloud data

2017 ◽  
Vol 7 (1.1) ◽  
pp. 64 ◽  
Author(s):  
S. Renu ◽  
S.H. Krishna Veni
Keyword(s):  
Data Storage ◽  
Binary Tree ◽  
Data Encryption ◽  
Third Party ◽  
Data Outsourcing ◽  
Cloud Data ◽  
Security Issues ◽  
Security Services ◽  
Computing Services ◽  
User Data

The Cloud computing services and security issues are growing exponentially with time. All the CSPs provide utmost security but the issues still exist. Number of technologies and methods are emerged and futile day by day. In order to overcome this situation, we have also proposed a data storage security system using a binary tree approach. Entire services of the binary tree are provided by a Trusted Third Party (TTP) .TTP is a government or reputed organization which facilitates to protect user data from unauthorized access and disclosure. The security services are designed and implemented by the TTP and are executed at the user side. Data classification, Data Encryption and Data Storage are the three vital stages of the security services. An automated file classifier classify unorganized files into four different categories such as Sensitive, Private, Protected and Public. Applied cryptographic techniques are used for data encryption. File splitting and multiple cloud storage techniques are used for data outsourcing which reduces security risks considerably. This technique offers  file protection even when the CSPs compromise. 

Byzantine Fault-Tolerant Architecture in Cloud Data Management

2019 ◽  
pp. 889-902
Author(s):  
Mohammed A. AlZain ◽  
Alice S. Li ◽  
Ben Soh ◽  
Mehedi Masud
Keyword(s):  
Cloud Computing ◽  
Data Management ◽  
Service Provider ◽  
Fault Tolerant ◽  
Academic Research ◽  
Point Of View ◽  
Cloud Data ◽  
Byzantine Faults ◽  
Byzantine Fault ◽  
Cloud Data Management

One of the main challenges in cloud computing is to build a healthy and efficient storage for securely managing and preserving data. This means a cloud service provider needs to make sure that its clients' outsourced data are stored securely and, data queries and retrievals are executed correctly and privately. On the other hand, it may also mean businesses are willing to outsource their data to a third party only if they trust their data are not accessible and visible to the service provider and other non-authorized parties. However, one of the major obstacles faced here for ensuring data reliability and security is Byzantine faults. While Byzantine fault tolerance (BFT) has received growing attention from the academic research community, the research done is generally from the distributed computing point of view, and hence finds little practical use in cloud computing. To that end, the focus of this paper is to discuss how these faults can be tolerated with the authors' proposed conceptualization of Byzantine data faults and fault-tolerant architecture in cloud data management.

Multi-Cloud Data Management Using Shamir's Secret Sharing and Quantum Byzantine Agreement Schemes

2016 ◽  
pp. 1205-1222
Author(s):  
Mohammed A. AlZain ◽  
Alice S. Li ◽  
Ben Soh ◽  
Eric Pardede
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Management ◽  
Data Storage ◽  
Secret Sharing ◽  
Byzantine Agreement ◽  
Management Model ◽  
Cloud Data ◽  
Cloud Data Management ◽  
Multi Cloud

Cloud computing is a phenomenal distributed computing paradigm that provides flexible, low-cost on-demand data management to businesses. However, this so-called outsourcing of computing resources causes business data security and privacy concerns. Although various methods have been proposed to deal with these concerns, none of these relates to multi-clouds. This paper presents a practical data management model in a public and private multi-cloud environment. The proposed model BFT-MCDB incorporates Shamir's Secret Sharing approach and Quantum Byzantine Agreement protocol to improve trustworthiness and security of business data storage, without compromising performance. The performance evaluation is carried out using a cloud computing simulator called CloudSim. The experimental results show significantly better performance in terms of data storage and data retrieval compared to other common cloud cryptographic based models. The performance evaluation based on CloudSim experiments demonstrates the feasibility of the proposed multi-cloud data management model.

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