scholarly journals An Efficient Privacy Preserving Search Scheme with Access Control for Cloud Data Centers

2016 ◽  
Vol 25 ◽  
pp. 310-317 ◽  
Author(s):  
V. Tresa Mary George ◽  
S. Shamna ◽  
Jubilant J. Kizhakkethottam
Author(s):  
T Gunasekhar ◽  
K Thirupathi Rao ◽  
V Krishna Reddy ◽  
P Sai Kiran ◽  
B Thirumala Rao

The malicious insider can be an employees, user and/or third party business partner. In cloud environment, clients may store sensitive data about their organization in cloud data centers. The cloud service provider should ensure integrity, security, access control and confidentiality about the stored data at cloud data centers. The malicious insiders can perform stealing on sensitive data at cloud storage and at organizations. Most of the organizations ignoring the insider attack because it is harder to detect and mitigate. This is a major emerging problem at the cloud data centers as well as in organizations. In this paper, we proposed a method that ensures security, integrity, access control and confidentiality on sensitive data of cloud clients by employing multi cloud service providers. The organization should encrypt the sensitive data with their security policy and procedures and store the encrypted data in trusted cloud. The keys which are used during encryption process are again encrypted and stored in another cloud area. So that organization contains only keys for keys of encrypted data. The Administrator of organization also does not know what data kept in cloud area and if he accesses the data, easily caught during the auditing. Hence, the only authorized used can access the data and use it and we can mitigate insider attacks by providing restricted privileges.


2017 ◽  
Vol 26 (1) ◽  
pp. 113-128
Author(s):  
Gamal Eldin I. Selim ◽  
Mohamed A. El-Rashidy ◽  
Nawal A. El-Fishawy

2021 ◽  
Vol 11 (9) ◽  
pp. 3870
Author(s):  
Jeongsu Kim ◽  
Kyungwoon Lee ◽  
Gyeongsik Yang ◽  
Kwanhoon Lee ◽  
Jaemin Im ◽  
...  

This paper investigates the performance interference of blockchain services that run on cloud data centers. As the data centers offer shared computing resources to multiple services, the blockchain services can experience performance interference due to the co-located services. We explore the impact of the interference on Fabric performance and develop a new technique to offer performance isolation for Hyperledger Fabric, the most popular blockchain platform. First, we analyze the characteristics of the different components in Hyperledger Fabric and show that Fabric components have different impacts on the performance of Fabric. Then, we present QiOi, component-level performance isolation technique for Hyperledger Fabric. The key idea of QiOi is to dynamically control the CPU scheduling of Fabric components to cope with the performance interference. We implement QiOi as a user-level daemon and evaluate how QiOi mitigates the performance interference of Fabric. The evaluation results demonstrate that QiOi mitigates performance degradation of Fabric by 22% and improves Fabric latency by 2.5 times without sacrificing the performance of co-located services. In addition, we show that QiOi can support different ordering services and chaincodes with negligible overhead to Fabric performance.


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