text: Added references

text/bibliography.bib

@@ -953,6 +953,13 @@
   organization = {IEEE}
 }
 
+@misc{katsomallos2016measuring,
+  title  = {Measuring Privacy Leakage under Continual Publication of Crowdsensing Data},
+  author = {Katsomallos, Manos and Christophides, Vassilis and Tzompanaki, Katerina and Kotzinos, Dimitris},
+  year   = {2016},
+  note   = {S{\~a}o Paulo School of Advanced Science on Smart Cities}
+}
+
 @inproceedings{katsomallos2017open,
   title        = {An open framework for flexible plug-in privacy mechanisms in crowdsensing applications},
   author       = {Katsomallos, Manos and Lalis, Spyros and Papaioannou, Thanasis and Theodorakopoulos, George},
@@ -972,6 +979,14 @@
   year    = {2019}
 }
 
+@inproceedings{katsomallos2022landmark,
+  title     = {Landmark privacy: Configurable differential privacy protection for time series},
+  author    = {Katsomallos, Manos and Tzompanaki, Katerina and Kotzinos, Dimitris},
+  booktitle = {Proceedings of the Twelfth ACM Conference on Data and Application Security and Privacy},
+  year      = {2022},
+  note      = {Under review}
+}
+
 @inproceedings{kellaris2013practical,
   title        = {Practical differential privacy via grouping and smoothing},
   author       = {Kellaris, Georgios and Papadopoulos, Stavros},
@@ -1065,6 +1080,20 @@
   isbn      = {0-201-03801-3}
 }
 
+@misc{kotzinos2016data,
+  title  = {Data Quality and User Privacy in Big GeoData: How does the one affect the other?},
+  author = {Katsomallos, Manos and Tzompanaki, Katerina and Kotzinos, Dimitris},
+  year   = {2016},
+  note   = {11th International Workshop on Information Search, Integration, and Personalization}
+}
+
+@misc{kotzinos2017data,
+  title  = {Data Quality Issues in Big GeoData: how does this affect privacy?},
+  author = {Katsomallos, Manos and Tzompanaki, Katerina and Kotzinos, Dimitris},
+  year   = {2017},
+  note   = {DaQuaTa International Workshop}
+}
+
 @inproceedings{krumm2013placer,
   title     = {Placer: semantic place labels from diary data},
   author    = {Krumm, John and Rouhana, Dany},
@@ -1286,6 +1315,15 @@
   organization = {ACM}
 }
 
+@inproceedings{meshgi2015expanding,
+  title        = {Expanding histogram of colors with gridding to improve tracking accuracy},
+  author       = {Meshgi, Kourosh and Ishii, Shin},
+  booktitle    = {2015 14th IAPR International Conference on Machine Vision Applications (MVA)},
+  pages        = {475--479},
+  year         = {2015},
+  organization = {IEEE}
+}
+
 @inproceedings{metwally2005duplicate,
   title     = {Duplicate detection in click streams},
   author    = {Metwally, Ahmed and Agrawal, Divyakant and El Abbadi, Amr},
@@ -1480,7 +1518,7 @@
   title        = {Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression},
   author       = {Samarati, Pierangela and Sweeney, Latanya},
   booktitle    = {IEEE Symposium on Research in Security and Privacy in},
-  year         = {1998},
+  year         = {2017},
   organization = {IEEE}
 }
 
@@ -1761,15 +1799,6 @@
   year      = {2017}
 }
 
-@inproceedings{meshgi2015expanding,
-  title={Expanding histogram of colors with gridding to improve tracking accuracy},
-  author={Meshgi, Kourosh and Ishii, Shin},
-  booktitle={2015 14th IAPR International Conference on Machine Vision Applications (MVA)},
-  pages={475--479},
-  year={2015},
-  organization={IEEE}
-}
-
 @inproceedings{wang2017privacy,
   title        = {Privacy Preserving Anonymity for Periodical SRS Data Publishing},
   author       = {Wang, Jie-Teng and Lin, Wen-Yang},

text/evaluation/summary.tex

@@ -1,5 +1,7 @@
 \section{Summary}
 \label{sec:eval-sum}
+\nnfootnote{This chapter is under review for being published in the proceedings of the $12$th ACM Conference on Data and Application Security and Privacy~\cite{katsomallos2022landmark}.}
+
 In this chapter we presented the experimental evaluation of the {\thething} privacy schemes and the privacy-preserving {\thething} selection scheme that we developed in Chapter~\ref{ch:lmdk-prv}, on real and synthetic data sets.
 The Adaptive scheme is the most reliable and best performing scheme, in terms of overall data utility, with minimal tuning across most of the cases.
 Skip performs optimally in data sets with a smaller target value range, where approximation fits best.

text/introduction/main.tex

@@ -1,5 +1,6 @@
 \chapter{Introduction}
 \label{ch:intro}
+\nnfootnote{This chapter was presented at the S{\~a}o Paulo School of Advanced Science on Smart Cities~\cite{katsomallos2016measuring}, as well as during the $11$th International Workshop on Information Search, Integration, and Personalization~\cite{kotzinos2016data} and at the DaQuaTa International Workshop~\cite{kotzinos2017data}.}
 
 Data privacy is becoming an increasingly important issue, both at a technical and at a societal level, and introduces various challenges ranging from the way we share and publish data sets to the way we use online and mobile services.
 Personal information, also described as \emph{microdata}, acquired increasing value and are in many cases used as the `currency'~\cite{economist2016data} to pay for access to various services, i.e.,~users are asked to exchange their personal information with the service provided.
@@ -46,7 +47,7 @@ Selecting the wrong privacy algorithm or configuring it poorly may put at risk t
 
 \begin{figure}[htp]
   \centering
-  \includegraphics[width=.5\linewidth]{introduction/data-value}
+  \includegraphics[width=.75\linewidth]{introduction/data-value}
   \caption{Value of data for decision-making over time from less than seconds to more than months~\cite{gualtieri2016perishable}.}
   \label{fig:data-value}
 \end{figure}

text/problem/summary.tex

@@ -1,5 +1,6 @@
 \section{Summary}
 \label{sec:lmdk-sum}
+\nnfootnote{This chapter was published in the proceedings of the $19$th Journal of Spatial Information Science~\cite{katsomallos2019privacy}.}
 
 In this chapter, we presented \emph{{\thething} privacy} for privacy-preserving time series publishing, which allows for the protection of significant events, while improving the utility of the final result with respect to the traditional user-level differential privacy.
 We also proposed three models for {\thething} privacy, and quantified the privacy loss under temporal correlation.