.png){kind=icon}
.png){kind=icon}
Return
A framework for utility enhanced incomplete microdata anonymization
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-017-0795-6.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Incomplete microdata, i.e., microdata with missing value, is very common in real-world datasets. However, existing anonymization techniques, which were developed for complete datasets, suffer from serious information loss on incomplete microdata, due to the missing value pollution. In this paper, we propose a framework for utility enhanced anonymization of incomplete microdata to address this issue. First, we study the properties of missing value pollution on generalization. Guided by these properties, we develop two top-down anonymization algorithms to preserve data utility on incomplete microdata. Extensive experiments on real-world datasets show that our techniques outperform the state-of-the-art techniques in terms of information loss and missing value pollution.
Keywords:
Data anonymization
Missing value
k-Anonymity
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
C
IF:
4.1
Papers:
5.0K
Citations:
7.5K