examples firts draft

.github/workflows/blank.yml

@@ -29,17 +29,12 @@ jobs:
       - name: Generate PDF document
         uses: hspaans/latexmk-action@v1
 
-      - name: Create Release
-        id: create_release
-        uses: actions/create-release@v1
-        env:
-          GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} # This token is provided by Actions, you do not need to create your own token
+      - uses: "marvinpinto/action-automatic-releases@latest"
         with:
-          tag_name: ${{ github.ref }}
-          release_name: Release ${{ github.ref }}
-          body: |
-            Changes in this Release
-            - First Change
-            - Second Change
-          draft: false
-          prerelease: false
+          repo_token: "${{ secrets.GITHUB_TOKEN }}"
+          automatic_release_tag: "latest"
+          prerelease: true
+          title: "Development Build"
+          files: |
+            *.pdf
+

macro.tex

@@ -43,6 +43,7 @@
 \newcommand{\templateChartAnnotation}{\ensuremath{\myLambda,\myGamma}}
 \newcommand{\instanceChartAnnotation}{\ensuremath{\myLambda}}
 \renewcommand{\P}[1]{\ensuremath{P_{#1}}}
+\newcommand{\p}[1]{\ensuremath{p_{#1}}}
 % \newcommand{\TF}{\ensuremath{T_{\fChartFunction}}}
 \newcommand{\user}{user\,}
 \newcommand{\User}{User\,}
@@ -57,4 +58,9 @@
 
 \newcommand{\pipeline}{Pipeline\xspace}
 \newcommand{\pipelineTemplate}{Pipeline Template\xspace}
-\newcommand{\pipelineInstance}{Pipeline Instance\xspace}
+\newcommand{\pipelineInstance}{Pipeline Instance\xspace}
+
+
+\newcommand{\policy}[5]{
+  $\langle \text{#1},{#2},{#3},{#4},\ensuremath{#5} \rangle$
+}

main.tex

@@ -24,6 +24,7 @@
 \usepackage{soul}
 \usepackage[inline]{enumitem}
 \usepackage{xcolor}
+\usepackage{amssymb}
 \graphicspath{{Images/}}
 \definecolor{commentsColor}{rgb}{0.497495, 0.497587, 0.497464}
 \definecolor{keywordsColor}{rgb}{0.000000, 0.000000, 0.635294}

pipeline_instance_example.tex

@@ -1,4 +1,3 @@
-
 \begin{example}\label{ex:instance}
 
   As an example, let us consider the pipeline template \tChartFunction in \cref{sec:example}.
@@ -8,52 +7,10 @@
   Services $s_1$ and $s_2$ are annotated with a profile that satisfies the data protection requirements in \P{1}, respectively.
   The third service $s_3$ is annotated with a profile that does not satisfy the data protection requirements in \P{1}.
   The filtering algorithm then returns the set $S'=\{s_1,s_2\}$.
-  The comparison algorithm is fnally applied to $S'$ and returns a ranking of the services according to quality metrics, where $s_1$ is ranked first. $s_1$ is then selected and integrated in $\vii{1}\in \Vp$.
+  The comparison algorithm is finally applied to $S'$ and returns a ranking of the services according to quality metrics, where $s_1$ is ranked first. $s_1$ is then selected and integrated in $\vii{1}\in \Vp$.
 
   The comparison algorithm is finally applied to $S'$ and returns a ranking of the services according to quality metrics, where $s_1$ is ranked first. $s_1$ is then selected and integrated in $\vii{1}\in \Vp$.
 
   The same logic is applied to the \vi{2} and \vi{3}.
 
-\end{example}
-
-
-% \begin{figure}[H]
-%   \centering
-
-%   \begin{tikzpicture}
-%     % Nodes
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node11) at (0,1.2) {Sx};
-%     \node[draw, circle, minimum size=1cm] (node1) at (0,0) {S1};
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node10) at (0,-1.2) {Sy};
-
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node22) at (2,1.2) {Sx};
-%     \node[draw, circle, minimum size=1cm] (node2) at (2,0) {S2};
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node21) at (2,-1.2) {Sy};
-
-%     \node[draw, circle, minimum size=1cm] (node3) at (4,0) {$\timesOperator$};
-
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node42) at (5,-1.5) {Sx};
-%     \node[draw, circle, minimum size=1cm] (node4) at (6,-1.5) {S3};
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node41) at (7,-1.5) {Sy};
-
-%     \node[draw, circle, minimum size=1cm] (node5) at (6,1.5) {S4};
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node51) at (5,1.5) {Sx};
-%     \node[draw, circle, minimum size=0.4cm, draw=gray, text opacity=0.5] (node52) at (7,1.5) {Sy};
-%     % Connection
-%     \draw[->] (node1) -- (node2);
-%     \draw[->] (node2) -- (node3);
-%     \draw[->] (node3) -- (node4);
-%     \draw[->] (node3) -- (node5);
-%   \end{tikzpicture}
-%   \caption{Service composition instance}
-%   \label{fig:service_composition_instance}
-% \end{figure}
-% \[ \forall S \in \mathrm{S}_{C}  \exists  \iChartFunction(S) = \mathrm{S}_{1} \]
-
-
-% \begin{figure}
-%   \centering
-%   \includegraphics[width=\columnwidth]{serviceDetail.pdf}
-%   \caption{Service Detail}
-%   \label{fig:service_detail}reinstall remote-ssh
-% \end{figure}
+\end{example}

pipeline_template_example.tex

@@ -1,22 +1,58 @@
 \subsection{Example}\label{sec:example}
-As an example, let us consider a pipeline template $G^{\myLambda,\myGamma}$ as a sequence of three vertices modeling three key stages in our reference scenario: data preparation (\vi{1}), data enrichment (\vi{2}), and data storage (\vi{3}). Each stage is annotated with its policy set \P{i} and functional description \F{i}. Table~\ref{table:example} reports the policies and functional descriptions for each vertex, which are presented in the following.
-
-%\begin{enumerate*}[label=n\arabic*)]
-%  \item
-The first vertex (\vi{1}) is responsible for data preparation.
-It specifies an anonymization policy ($\myLambda(v_1)$) to protect sensitive information, such as personally identifiable information (PII) in the dataset.
-The transformation function \TF{1} in $\myGamma(v_1)$ is an empty function \tf{a}, as no functional transformation is required for anonymization.
-%   \item
-
-The second vertex (\vi{2}) focuses on data enrichment, where additional information from the states of New York and New Hampshire is integrated into the dataset.
-It requires a data enrichment policy ($\myLambda(v_2)$) to ensure that the added data are relevant and comply with privacy regulations.
-The transformation function \TF{2} in $\myGamma(v_2$) is an additive function \tf{a}, which merges and integrates the external data with the existing dataset.
-  %  \item
-
-  The third vertex (\vi{3}) is responsible for aggregating data, including statistical measures like averages, medians, and some more statistics.
-  It follows an aggregation policy ($\myLambda(v_3)$) to define how the aggregation should be performed, and ensure compliance with privacy and security regulations.
-  The transformation function \TF{3} in $\myGamma(v_3)$ is a transformation function \tf{t}, which computes the required statistics and aggregates the data.
-%\end{enumerate*}
+
+
+As an example, let us consider the pipeline template \tChartFunction in \cref{sec:example}.
+
+In this example all the policies referenced are detailed in table \ref{tab:anonymization}.
+We assume that the data owner is the Connecticut Prison (CTP) and that this facility has a partnership with two others, namely New York Prison and New Hampshire Prison.
+
+The first stage is responsible for data anonymization. The  policy annotation \P{i}, associated with the first stage,
+specifies three degree of anonymization : \emph{none}, \emph{partial}, and \emph{full}.
+During service execution, the policy is assessed:
+if the service profile match with the data owner, \P{1} is satisfied and the data is not anonymized;
+if the service profile match with a partner of the owner, \P{2} is satisfied and the data is partially anonymized;
+if the service profile doesn't match with a partner nor with the owner, \P{3} is satisfied and the data is fully anonymized.
+
+The second stage is responsible for data enrichment. The policy annotation $p$, associated with the second stage,
+
+The third stage, responsible for data analysis and statistics, adopts policies analogous to the first stage. The logic remains consistent:
+if the service profile match with the data owner, \P{1} is satisfied and the data computation is made on non anonymized data;
+if the service profile match with a partner of the owner, \P{2} is satisfied and the data computation is made on partially anonymized data;
+if the service profile doesn't match with a partner nor with the owner, \P{3} is satisfied and the data computation is made on fully anonymized data.
+The fourth stage is responsible for machine learning tasks:
+policy \p{4} applies, thus we always aim to anonymize the dataset to avoid any personal identifiers entering into the machine learning algorithm/model.
+The fifth stage is responsible for storing data. As stated in policy annotation \P{5}, if the service is located in the facility itself, the data will not be anonymized.
+If the service is located in a partner region, the data will be partially anonymized.
+
+The sixth stage is responsible for data visualization. As stated in policy annotation \P{6}, if the user is member of the facility itself, the data will not be anonymized.
+If the user is member of a partner facility, the data will be partially anonymized.
+If the user is not member of the facility nor a partner, the data will be fully anonymized.
+
+
+
+
+
+\begin{table*}[ht!]
+  \centering
+  \bgroup
+  \def\arraystretch{1.5}
+  \begin{tabular}{c|c||l}
+    \textbf{Policy} & \textbf{Service} & \textbf{Anonymization}                                                                                \\ \hline
+
+    $\p{1}$         & \s{1}            & \policy{$\langle service,owner=``CTP"\rangle$}{dataset}{READ}{ANY}{\varnothing}                       \\
+    $\p{2}$         & \s{1}            & \policy{$\langle service,owner=partner(``CTP") \rangle$}{dataset}{READ}{ANY}{light\_anonymization}    \\
+    $\p{3}$         & \s{1}            & \policy{$\langle service,owner=``Connecticut Prison"$}{dataset}{READ}{ANY}{full\_anonymization}       \\
+    $\p{4}$         & \s{4}            & \policy{ANY}{dataset}{READ}{ANY}{full\_anonymization}                                                 \\
+    $\p{5}$         & \s{5}            & \policy{$\langle service,region=``FACILITY"\rangle$}{dataset}{WRITE}{ANY}{none}                       \\
+    $\p{6}$         & \s{5}            & \policy{$\langle service,region=``{CT,NY,NH}"\rangle$}{dataset}{WRITE}{ANY}{light\_anonymization}     \\
+    $\p{7}$         & \s{6}            & \policy{$\langle,user,role=   ``Connecticut Prison Officer"$}{dataset} {READ}{ANY}{none}              \\
+    $\p{7}$         & \s{6}            & \policy{$\langle,user,role=   ``Partener Prison Officer"$}{dataset} {READ}{ANY}{light\_anonymization} \\
+    $\p{8}$         & \s{6}            & \policy{$\langle,user,role=   ``Any"$}{dataset} {READ}{ANY}{full\_anonymization}                      \\
+  \end{tabular}
+  \egroup
+  \caption{Anonymization policies}
+  \label{tab:anonymization}
+\end{table*}
 
 \begin{figure}[ht!]
   \centering