Paper Info
Title
Correlation hiding by independence masking
Abstract
Extracting useful correlation from a dataset has been extensively studied. In this paper, we deal with the opposite, namely, a problem we call correlation hiding (CH), which is fundamental in numerous applications that need to disseminate data containing sensitive information. In this problem, we are given a relational table T whose attributes can be classified into three disjoint sets A, B, and C. The objective is to distort some values in T so that A becomes independent from B, and yet, their correlation with C is preserved as much as possible. CH is different from all the problems studied previously in the area of data privacy, in that CH demands complete elimination of the correlation between two sets of attributes, whereas the previous research focuses on partial elimination up to a certain level. A new operator called independence masking is proposed to solve the CH problem. Implementations of the operator with good worst case guarantees are described in the full version of this short note.
Year
DOI
Venue
2010
10.1109/ICDE.2010.5447849
ICDE
Keywords
Field
DocType
data privacy,approximation theory,relational table,independence masking,data encapsulation,computational complexity,data dissemination,data mining,correlation hiding,minimisation,privacy,correlation,association rules,government,databases,approximation algorithms
Approximation algorithm,Data mining,Disjoint sets,Computer science,Approximation theory,Theoretical computer science,Association rule learning,Minimisation (psychology),Operator (computer programming),Information privacy,Database,Computational complexity theory
Conference
ISSN
ISBN
Citations 
1084-4627
978-1-4244-5444-0
7
PageRank 
References 
Authors
0.47
6
6
Name
Order
Citations
PageRank
Yufei Tao17231316.71
Jian Pei219002995.54
Jiexing Li321110.36
Xiaokui Xiao43266142.32
Ke Yi51233.33
Zhengzheng Xing633213.73