diff --git a/tables/statistical.tex b/tables/statistical.tex
index 26ad5e2..ed4049b 100644
--- a/tables/statistical.tex
+++ b/tables/statistical.tex
@@ -78,6 +78,11 @@
       \hyperlink{chen2017pegasus}{\emph{PeGaSus}} & infinite & streaming & global & event & linkage & perturbation & differential \\
       \cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\ \hdashline
 
+      \hyperlink{errounda2018continuous}{\textbf{Errounda et al.}} & infinite & streaming & local & $w$-event & linkage & randomization (ran- & differential \\
+      \cite{errounda2018continuous} & & & & & & domized response), & privacy \\
+      & & & & & & perturbation & \\
+      & & & & & & (Laplace) & \\ \hdashline
+
       \hyperlink{wang2018privacy}{\textbf{\emph{DP-PSP}}} & infinite & streaming & global & $w$-event & linkage & perturbation (ex- & differential \\
       \cite{wang2018privacy} & & & & & & ponential, Laplace) & privacy \\
 
diff --git a/text/bibliography.bib b/text/bibliography.bib
index 204c1db..6e34dd0 100644
--- a/text/bibliography.bib
+++ b/text/bibliography.bib
@@ -565,6 +565,15 @@
   organization = {ACM}
 }
 
+@inproceedings{errounda2018continuous,
+  title        = {Continuous location statistics sharing algorithm with local differential privacy},
+  author       = {Errounda, Fatima Zahra and Liu, Yan},
+  booktitle    = {2018 IEEE International Conference on Big Data (Big Data)},
+  pages        = {5147--5152},
+  year         = {2018},
+  organization = {IEEE}
+}
+
 @online{experian,
   title = {Experian},
   note  = {\url{https://experian.com}.
diff --git a/text/related/statistical.tex b/text/related/statistical.tex
index 8c78323..85621ac 100644
--- a/text/related/statistical.tex
+++ b/text/related/statistical.tex
@@ -355,6 +355,22 @@ The data-adaptive Grouper consumes the original stream and partitions the data i
 Finally, a query specific Smoother combines the independent information produced by the Perturber and the Grouper, and performs post-processing by calculating the final estimates of the Perturber's values for each partition created by the Grouper at each timestamp.
 The combination of the Perturber and the Grouper follows the sequential composition and post-processing properties of differential privacy, thus, the resulting algorithm satisfies ($\varepsilon_p + \varepsilon_g$)-differential privacy.
 
+% Continuous location statistics sharing algorithm with local differential privacy
+% - statistical
+% - infinite
+% - streaming
+% - linkage
+% - local
+% - w-event
+% - differential privacy
+% - perturbation (randomized response, Laplace)
+\hypertarget{errounda2018continuous}{Errounda et al.}~\cite{errounda2018continuous} proposed a algorithm for sharing w-event local differentially private statistics over infinite streams of location data.
+The decision mechanism determines the similarity between the current data of every individual and the most recent release, with respect to a predefined threshold.
+Using the randomized response mechanism, it perturbs the result of this comparison and decides whether to perform an approximation based on the most recent release or calculate and release the current statistics after injecting to them Laplacian noise.
+Within the sliding window of size $w$, the privacy budget allocation mechanism estimates the overall privacy budget that the algorithm has allocated at any timestamp and decides how to optimally allocate the remaining budget in the future timestamps.
+The evaluation of the algorithm show that, according to the relevant literature on local differential privacy, the author's work achieves the the same utility as the centralized
+approach of differential privacy.
+
 % Privacy-protected statistics publication over social media user trajectory streams
 % - statistical
 % - infinite