scholarly journals Using Feature Selection to Improve the Utility of Differentially Private Data Publishing

2014 ◽  
Vol 37 ◽  
pp. 511-516 ◽  
Author(s):  
Yasser Jafer ◽  
Stan Matwin ◽  
Marina Sokolova
Keyword(s):  
Feature Selection ◽  
Data Publishing ◽  
Private Data

scholarly journals Privacy preserving data publishing of categorical data through k ‐anonymity and feature selection

2016 ◽  
Vol 3 (1) ◽  
pp. 16-21 ◽  
Author(s):  
Aristos Aristodimou ◽  
Athos Antoniades ◽  
Constantinos S. Pattichis
Keyword(s):  
Feature Selection ◽  
Categorical Data ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Privacy Preserving Data Publishing

scholarly journals Associated Attribute-Aware Differentially Private Data Publishing via Microaggregation

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 79158-79168 ◽  
Author(s):  
Gaoming Yang ◽  
Xinxin Ye ◽  
Xianjin Fang ◽  
Rongshi Wu ◽  
Li Wang
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially Private Data Publishing: Non-interactive Setting

2017 ◽  
pp. 35-48
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Wanlei Zhou ◽  
Philip S. Yu
Keyword(s):  
Data Publishing ◽  
Private Data

Efficient and Secure Outsourcing of Differentially Private Data Publishing with Multiple Evaluators

2020 ◽  
pp. 1-1
Author(s):  
Jin Li ◽  
Heng Ye ◽  
Tong Li ◽  
Wei Wang ◽  
Wenjing Lou ◽  
...  
Keyword(s):  
Data Publishing ◽  
Secure Outsourcing ◽  
Private Data

scholarly journals Data mining as a tool in privacy-preserving data publishing

2010 ◽  
Vol 45 (1) ◽  
pp. 151-159 ◽  
Author(s):  
Michal Sramka
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Privacy Preserving ◽  
Data Publishing ◽  
Sensitive Information ◽  
Private Data ◽  
Privacy Preserving Data Publishing

ABSTRACTMany databases contain data about individuals that are valuable for research, marketing, and decision making. Sharing or publishing data about individuals is however prone to privacy attacks, breaches, and disclosures. The concern here is about individuals’ privacy-keeping the sensitive information about individuals private to them. Data mining in this setting has been shown to be a powerful tool to breach privacy and make disclosures. In contrast, data mining can be also used in practice to aid data owners in their decision on how to share and publish their databases. We present and discuss the role and uses of data mining in these scenarios and also briefly discuss other approaches to private data analysis.

Differentially Private Data Publishing: Settings and Mechanisms

2017 ◽  
pp. 17-21
Author(s):  
Tianqing Zhu ◽  
Gang Li ◽  
Wanlei Zhou ◽  
Philip S. Yu
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially private data publishing via optimal univariate microaggregation and record perturbation

2018 ◽  
Vol 153 ◽  
pp. 78-90 ◽  
Author(s):  
Jordi Soria-Comas ◽  
Josep Domingo-Ferrer
Keyword(s):  
Data Publishing ◽  
Private Data

Differentially private data publishing via cross-moment microaggregation

2020 ◽  
Vol 53 ◽  
pp. 269-288 ◽  
Author(s):  
Javier Parra-Arnau ◽  
Josep Domingo-Ferrer ◽  
Jordi Soria-Comas
Keyword(s):  
Data Publishing ◽  
Private Data
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