correlation: Reviewed wang2021current

text/bibliography.bib

@@ -1664,6 +1664,16 @@
   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,
   title     = {Randomized response: A survey technique for eliminating evasive answer bias},
   author    = {Warner, Stanley L},

text/preliminaries/correlation.tex

@@ -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.
 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}
 \label{subsec:cor-temp}