scholarly journals Privacy-Preserving Clustering: A New Approach Based on Invariant Order Encryption

2020 ◽  
Vol 3 (2) ◽  
pp. 65-72
Author(s):  
Mihail-Iulian Pleșa ◽  
Cezar Pleșca
Keyword(s):  
Privacy Preserving ◽  
New Approach ◽  
Invariant Order

scholarly journals A New Approach to Privacy-Preserving Multiple Independent Data Publishing

2018 ◽  
Vol 8 (5) ◽  
pp. 783 ◽  
Author(s):  
A Hasan ◽  
Qingshan Jiang ◽  
Hui Chen ◽  
Shengrui Wang
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
Independent Data ◽  
New Approach

scholarly journals Slicing: A New Approach for Privacy Preserving Data Publishing

2012 ◽  
Vol 24 (3) ◽  
pp. 561-574 ◽  
Author(s):  
Tiancheng Li ◽  
Ninghui Li ◽  
Jian Zhang ◽  
Ian Molloy
Keyword(s):  
Privacy Preserving ◽  
Data Publishing ◽  
New Approach ◽  
Privacy Preserving Data Publishing

Privacy Preserving Clustering for Distributed Homogeneous Gene Expression Data Sets

2010 ◽  
Vol 1 (3) ◽  
pp. 31-54 ◽  
Author(s):  
Xin Li
Keyword(s):  
Privacy Preservation ◽  
Cluster Formation ◽  
Genomic Data ◽  
Principal Component ◽  
Privacy Preserving ◽  
Gene Clustering ◽  
Data Sets ◽  
Data Set ◽  
New Approach ◽  
Gene Profiles

In this paper, the authors present a new approach to perform principal component analysis (PCA)-based gene clustering on genomic data distributed in multiple sites (horizontal partitions) with privacy protection. This approach allows data providers to collaborate together to identify gene profiles from a global viewpoint, and at the same time, protect the sensitive genomic data from possible privacy leaks. The authors developed a framework for privacy preserving PCA-based gene clustering, which includes two types of participants such as data providers and a trusted central site. Within this mechanism, distributed horizontal partitions of genomic data can be globally clustered with privacy preservation. Compared to results from centralized scenarios, the result generated from distributed partitions achieves 100% accuracy by using this approach. An experiment on a real genomic data set is conducted, and result shows that the proposed framework produces exactly the same cluster formation as that from the centralized data set.

A New Privacy Preserving Approach Used in Cloud Computing

2010 ◽  
Vol 439-440 ◽  
pp. 1318-1323
Author(s):  
Jian Wang ◽  
Yan Zhao ◽  
Jia Jin Le
Keyword(s):  
Cloud Computing ◽  
Service Provider ◽  
Privacy Preserving ◽  
The Internet ◽  
Process Information ◽  
Privacy And Security ◽  
New Approach ◽  
Software Applications ◽  
Processing Power ◽  
Private Data

Cloud computing seems to offer some incredible benefits for communicators: the availability of an incredible array of software applications, access to lightning-quick processing power, unlimited storage, and the ability to easily share and process information. All of this is available through your browser any time you can access the Internet. While this might all appear enticing, there remain issues of reliability, portability, privacy, and security. When our private data are out-sourced in cloud computing, we should guarantee the confidentiality and searchability of the private data. Our paper provides a new approach to avoid the disclosure of the sensitive attributes of users when user ask for service from the Service Provider (SP) in cloud computing.

A new approach to privacy-preserving query in semi-honest model

2011 ◽  
Vol 3 (9) ◽  
pp. 294-301
Author(s):  
Tao Geng ◽  
Lei Pang ◽  
Shoushan Luo ◽  
Yang Xin ◽  
Yixian Yang
Keyword(s):  
Privacy Preserving ◽  
New Approach
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