Survey on security and privacy preserving public auditing for content storage in cloud environment

Author(s):  
Arun Kumar.K ◽  
Gnanadeepa.S ◽  
Hepzibha John ◽  
Janani.G.K
2020 ◽  
pp. 34-47
Author(s):  
Gomathy B ◽  
Ramesh SM ◽  
Shanmugavadivel G

A systematic and comprehensive review of privacy preserving and security challenges in cloud environment is presented in this literature. Since, cloud supports various applications, it requires immediate attention for serious security and privacy issues. Research must focus on efficient security mechanism for cloud-based networks, also it is essential to explore the techniques to maintain the integrity and confidentiality of the data. This paper highlights research challenges and directions concerning the security as a comprehensive study through intensive analysis of various literatures in the last decade, and it is summarized in terms of architecture types, evaluation strategies and security model. We surveyed, investigated and reviewed the articles in every aspect related to security and privacy preserving concepts and identified the following tasks: 1) architecture of wireless body area networks in cloud, 2) security and privacy in cloud based WBAN, 3), Cloud security and privacy issues in cloud 4) diverse authentication and cryptographic approaches, 4) optimization strategies to improve the security performance.


2012 ◽  
Vol 3 (3) ◽  
pp. 60-61
Author(s):  
V.Sajeev V.Sajeev ◽  
◽  
R.Gowthamani R.Gowthamani

2017 ◽  
Vol 96 (2) ◽  
pp. 2305-2322 ◽  
Author(s):  
Lei Zhang ◽  
Jing Li ◽  
Songtao Yang ◽  
Bin Wang

2019 ◽  
Vol 127 ◽  
pp. 59-69 ◽  
Author(s):  
Hui Tian ◽  
Fulin Nan ◽  
Chin-Chen Chang ◽  
Yongfeng Huang ◽  
Jing Lu ◽  
...  

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiru Li ◽  
Wei Xu ◽  
Huibin Shi ◽  
Yuanyuan Zhang ◽  
Yan Yan

Considering the importance of energy in our lives and its impact on other critical infrastructures, this paper starts from the whole life cycle of big data and divides the security and privacy risk factors of energy big data into five stages: data collection, data transmission, data storage, data use, and data destruction. Integrating into the consideration of cloud environment, this paper fully analyzes the risk factors of each stage and establishes a risk assessment index system for the security and privacy of energy big data. According to the different degrees of risk impact, AHP method is used to give indexes weights, genetic algorithm is used to optimize the initial weights and thresholds of BP neural network, and then the optimized weights and thresholds are given to BP neural network, and the evaluation samples in the database are used to train it. Then, the trained model is used to evaluate a case to verify the applicability of the model.


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