statistical: Reviewed ma2019real

tables/statistical.tex

@@ -79,7 +79,10 @@
       \cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\ \hdashline
 
       \hyperlink{farokhi2020temporally}{Farokhi} & infinite & streaming & global & - & linkage & perturbation & differential \\
-      \cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\
+      \cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\ \hdashline
+
+      \hyperlink{ma2019real}{\textbf{\emph{RPTR}}} & infinite & streaming & global & $w$-event & linkage & perturbation & differential \\
+      \cite{ma2019real} & & & & & & (Laplace) & privacy \\
 
       \bottomrule
 

text/bibliography.bib

@@ -838,7 +838,6 @@
   publisher = {IEEE}
 }
 
-
 @article{jain2016big,
   title     = {Big data privacy: a technological perspective and review},
   author    = {Jain, Priyank and Gyanchandani, Manasi and Khare, Nilay},
@@ -850,6 +849,7 @@
   publisher = {Springer}
 }
 
+
 @article{ji2014differential,
   title   = {Differential privacy and machine learning: a survey and review},
   author  = {Ji, Zhanglong and Lipton, Zachary C and Elkan, Charles},
@@ -1180,6 +1180,17 @@
   publisher = {IEEE}
 }
 
+@article{ma2019real,
+  title     = {Real-time privacy-preserving data release over vehicle trajectory},
+  author    = {Ma, Zhuo and Zhang, Tian and Liu, Ximeng and Li, Xinghua and Ren, Kui},
+  journal   = {IEEE transactions on vehicular technology},
+  volume    = {68},
+  number    = {8},
+  pages     = {8091--8102},
+  year      = {2019},
+  publisher = {IEEE}
+}
+
 @inproceedings{machanavajjhala2006diversity,
   title        = {l-diversity: Privacy beyond k-anonymity},
   author       = {Machanavajjhala, Ashwin and Gehrke, Johannes and Kifer, Daniel and Venkitasubramaniam, Muthuramakrishnan},

text/related/statistical.tex

@@ -372,3 +372,20 @@ Increasing the discount factor offers stronger privacy protection, equivalent to
 Whereas, increasing the discount coefficient resembles the behavior of event-level differential privacy.
 Selecting a suitable value for the privacy budget and the discount parameter allows for bounding the overall privacy loss in an infinite observation scenario.
 However, the assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios for statistical data.
+
+% Real-Time Privacy-Preserving Data Release Over Vehicle Trajectory
+% - statistical
+% - infinite
+% - streaming
+% - linkage
+% - global
+% - w-event
+% - differential privacy
+% - perturbation (Laplace)
+\hypertarget{ma2019real}{Ma et al.}~\cite{ma2019real} implemented \emph{RPTR}, a $w$-event differential privacy mechanism for protecting statistics of vehicular trajectory data in real time.
+RPTR adapts the rate with which it samples data according to the accuracy with which it can predict future statistics based on historical data and position transfer probability matrix and according to how much the original data change through time based on Pearson coefficient.
+Before releasing data statistics, the mechanism perturbs the original values with Laplacian noise the impact of which is mitigated by using Ensemble Kalman filtering.
+The combination of adaptive sampling and filtering can improve the accuracy when predicting the values of non-sampled data points, and thus saving more privacy budget (i.e.,~higher data utility) for data points that the mechanism decides to release.
+The mechanism detects highly frequented map regions and, using a quad-tree, it calculate the each region's privacy weight.
+In their implementation, the authors assume that highly frequented regions tend to be more privacy sensitive, and thus more noise (i.e.,~less privacy budget to invest) needs to be introduced before publicly releasing the users' data falling into these regions.
+The efficiency (both in terms of user privacy and data utility) of the mechanism depends on the number of regions that it divides the map, and therefore the challenge of its optimal division is an interesting future research topic.