manos/the-last-thing
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

correlation: Reviewed wang2021current

Browse Source
This commit is contained in:
Manos Katsomallos 2021-08-31 13:15:31 +03:00
parent e96ed6505f
commit 0650b710b5
2 changed files with 14 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

10
text/bibliography.bib
View File

@ -1664,6 +1664,16 @@
organization = {IEEE} organization = {IEEE}
} }
@article{wang2021current,
title = {Why current differential privacy schemes are inapplicable for correlated data publishing?},
author = {Wang, Hao and Xu, Zhengquan and Jia, Shan and Xia, Ying and Zhang, Xu},
journal = {World Wide Web},
volume = {24},
pages = {1--23},
year = {2021},
publisher = {Springer}
}
@article{warner1965randomized, @article{warner1965randomized,
title = {Randomized response: A survey technique for eliminating evasive answer bias}, title = {Randomized response: A survey technique for eliminating evasive answer bias},
author = {Warner, Stanley L}, author = {Warner, Stanley L},

4
text/preliminaries/correlation.tex
View File

@ -60,6 +60,10 @@ A negative value shows that the behavior of one variable is the \emph{opposite}
Zero means that the variables are not linked and are \emph{independent} of each other. Zero means that the variables are not linked and are \emph{independent} of each other.
A positive correlation indicates that the variables behave in a \emph{similar} manner, e.g.,~when the one decreases the other decreases as well. A positive correlation indicates that the variables behave in a \emph{similar} manner, e.g.,~when the one decreases the other decreases as well.
Wand et al.~\cite{wang2021current} examined why current differential privacy methods that either increase the noise size to offset the privacy leakage caused by the correlation (model-based) or transform correlated data into independent series to another domain and process them independently (transform-based) are inapplicable for correlated data publishing.
They prove that the privacy distortion, which they quantify using entropy, after filtering out the independent and identically distributed noise from the correlated data by utilizing the data correlation (correlation-distinguishability attack) is equal to that of conditional probability inference.
They conclude that the problem stems from the difference of correlation between the noise that the current methods inject and the output data.
\subsection{Privacy loss under temporal correlation} \subsection{Privacy loss under temporal correlation}
\label{subsec:cor-temp} \label{subsec:cor-temp}