scholarly journals Secure Ring Signature based privacy preserving of Public Auditing mechanism for outsourced data in cloud computing paradigm

2021 ◽  
Vol 1916 (1) ◽  
pp. 012079
Author(s):  
D Srivaishnavi ◽  
T Arjun ◽  
K Dhyaneshwaran ◽  
R Deepak
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Ring Signature ◽  
Public Auditing ◽  
Computing Paradigm ◽  
Outsourced Data

Privacy-preserving public auditing for secure data storage in fog-to-cloud computing

2019 ◽  
Vol 127 ◽  
pp. 59-69 ◽  
Author(s):  
Hui Tian ◽  
Fulin Nan ◽  
Chin-Chen Chang ◽  
Yongfeng Huang ◽  
Jing Lu ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Privacy Preserving ◽  
Public Auditing ◽  
Secure Data

Privacy Preserving and Public Auditing Service for Data Storage in Cloud Computing

2012 ◽  
Vol 2 (2) ◽  
pp. 131-133 ◽  
Author(s):  
V. Tejaswini V. Tejaswini ◽  
◽  
K. Sunitha K. Sunitha ◽  
S.K. Prashanth S.K. Prashanth
Keyword(s):  
Cloud Computing ◽  
Data Storage ◽  
Privacy Preserving ◽  
Public Auditing

scholarly journals PPK-Means: Achieving Privacy-Preserving Clustering Over Encrypted Multi-Dimensional Cloud Data

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 310 ◽  
Author(s):  
Hui Yin ◽  
Jixin Zhang ◽  
Yinqiao Xiong ◽  
Xiaofeng Huang ◽  
Tiantian Deng
Keyword(s):  
Cloud Computing ◽  
Data Clustering ◽  
Data Privacy ◽  
Real Life ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Data Sets ◽  
Cloud Data ◽  
Practical Applications ◽  
Computing Paradigm

Clustering is a fundamental and critical data mining branch that has been widely used in practical applications such as user purchase model analysis, image color segmentation, outlier detection, and so on. With the increasing popularity of cloud computing, more and more encrypted data are converging to cloud computing platforms for enjoying the revolutionary advantages of the cloud computing paradigm, as well as mitigating the deeply concerned data privacy issues. However, traditional data encryption makes existing clustering schemes no more effective, which greatly obstructs effective data utilization and frustrates the wide adoption of cloud computing. In this paper, we focus on solving the clustering problem over encrypted cloud data. In particular, we propose a privacy-preserving k-means clustering technology over encrypted multi-dimensional cloud data by leveraging the scalar-product-preserving encryption primitive, called PPK-means. The proposed technique is able to achieve efficient multi-dimensional data clustering as well to preserve the confidentiality of the outsourced cloud data. To the best of our knowledge, our work is the first to explore the privacy-preserving multi-dimensional data clustering in the cloud computing environment. Extensive experiments in simulation data-sets and real-life data-sets demonstrate that our proposed PPK-means is secure, efficient, and practical.

BioGamal Privacy Preserving Public Auditing for Cloud Computing

2021 ◽  
pp. 129-140
Author(s):  
Utkarsh Pandey ◽  
Bhavana Verma ◽  
Chetan Agarwal
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Public Auditing

Privacy-preserving public auditing for educational multimedia data in cloud computing

2015 ◽  
Vol 75 (21) ◽  
pp. 13077-13091 ◽  
Author(s):  
Daeyeong Kim ◽  
Hyunsoo Kwon ◽  
Changhee Hahn ◽  
Junbeom Hur
Keyword(s):  
Cloud Computing ◽  
Privacy Preserving ◽  
Multimedia Data ◽  
Public Auditing ◽  
Educational Multimedia

scholarly journals Privacy preserving outsourcing algorithm for two-point linear boundary value problems

2019 ◽  
Vol 16 (2) ◽  
pp. 1065
Author(s):  
Nedal Mohammed ◽  
Laman R. Sultan ◽  
Santosh Lomte
Keyword(s):  
Cloud Computing ◽  
Boundary Value Problems ◽  
Service Providers ◽  
Boundary Value ◽  
Privacy Preserving ◽  
Point Boundary ◽  
Storage Devices ◽  
Computing Paradigm ◽  
Scientific Computations ◽  
Outsourcing Algorithm

<p>One of a powerful application in the age of cloud computing is the outsourcing of scientific computations to cloud computing which makes cloud computing a very powerful computing paradigm, where the customers with limited computing resource and storage devices can outsource the sophisticated computation workloads into powerful service providers. One of scientific computations problem is Two-Point Boundary Value Problems(BVP) is a basic engineering and scientific problem, which has application in various domains. In this paper, we propose a privacy-preserving, verifiable and efficient algorithm for Two-Point Boundary Value Problems in outsourcing paradigm. We implement the proposed schema on the customer side laptop and using AWS compute domain elastic compute cloud (EC2) for the cloud side.</p>

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