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evaluation: lmdk-sel-sol

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text/problem/theotherthing/solution.tex
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\label{subsec:lmdk-sel-sol} \label{subsec:lmdk-sel-sol}
The main idea of the privacy-preserving {\thething} selection component is to privately select extra {\thething} event timestamps, i.e.,~dummy {\thethings}, from the set of timestamps $T /\ L$ of the time series $S_T$ and add them to the original {\thething} set $L$. The main idea of the privacy-preserving {\thething} selection component is to privately select extra {\thething} event timestamps, i.e.,~dummy {\thethings}, from the set of timestamps $T /\ L$ of the time series $S_T$ and add them to the original {\thething} set $L$.
Selecting extra events, on top of the actual {\thethings}, as dummy {\thethings} can render actual ones indistinguishable.
The goal is to select a list of sets with additional timestamps from a series of events at timestamps $T$ for a set of {\thethings} at $L \subseteq T$.
Thus, we create a new set $L'$ such that $L \subset L' \subseteq T$. Thus, we create a new set $L'$ such that $L \subset L' \subseteq T$.
We generate a set of dummy {\thething} set options by adding regular event timestamps from $T /\ L$ to $L$ (Section~\ref{subsec:lmdk-set-opts}).
Then (Section~\ref{subsec:lmdk-opt-sel}), we utilize the exponential mechanism, with a utility function that calculates an indicator for each of the options in the set based on how much it differs from the original {\thething} set $L$, and randomly select one ot the options that we created earlier. First, we generate a set of dummy {\thething} set options by adding regular event timestamps from $T /\ L$ to $L$ (Section~\ref{subsec:lmdk-set-opts}).
Then, we utilize the exponential mechanism, with a utility function that calculates an indicator for each of the options in the set based on how much it differs from the original {\thething} set $L$, and randomly select one of the options (Section~\ref{subsec:lmdk-opt-sel}).
This process provides an extra layer of privacy protection to {\thethings}, and thus allows the release, and thereafter processing, of {\thething} timestamps. This process provides an extra layer of privacy protection to {\thethings}, and thus allows the release, and thereafter processing, of {\thething} timestamps.
% We utilize the exponential mechanism with a utility function that calculates an indicator for each of the options in the set that we selected in the previous step. % We utilize the exponential mechanism with a utility function that calculates an indicator for each of the options in the set that we selected in the previous step.
% The utility depends on the positioning of the {\thething} timestamps of an option in the series, e.g.,~the distance from the previous/next {\thething}, the distance from the start/end of the series, etc. % The utility depends on the positioning of the {\thething} timestamps of an option in the series, e.g.,~the distance from the previous/next {\thething}, the distance from the start/end of the series, etc.
\subsubsection{{\Thething} set options} \subsubsection{{\Thething} set options generation}
\label{subsec:lmdk-set-opts} \label{subsec:lmdk-set-opts}
This step aims to select a set of candidate {\thething} timestamps options either by randomizing the actual timestamps (Section~\ref{subsec:lmdk-rnd}), or by inserting dummy timestamps (Section~\ref{subsec:lmdk-dum-gen}) to the actual {\thething} timestamps.
\paragraph{Dummy {\thething} generation}
\label{subsec:lmdk-dum-gen}
Selecting extra events, on top of the actual {\thethings}, as dummy {\thethings} can render actual ones indistinguishable.
The goal is to select a list of sets with additional timestamps from a series of events at timestamps $\{t_n\}$ for a set of {\thethings} at $\{l_k\} \subseteq \{t_n\}$.
Algorithms~\ref{algo:lmdk-sel-opt} and \ref{algo:lmdk-sel-heur} approach this problem with an optimal and heuristic methodology, respectively. Algorithms~\ref{algo:lmdk-sel-opt} and \ref{algo:lmdk-sel-heur} approach this problem with an optimal and heuristic methodology, respectively.
Function \evalSeq evaluates the result of the union of $L$ and a timestamp combination from $T \setminus L$ by, e.g.,~estimating the standard deviation of all the distances from the previous/next {\thething}.
\getOpts returns all the possible \emph{valid} sets of combinations \opt such that larger options contain all of the timestamps that are present in smaller ones.
Each combination contains a set of timestamps with sizes $\left|L\right| + 1, \left|L\right| + 2, \dots, \left|T\right|$, where each one of them is a combination of $L$ with $x \in [1, \left|T\right| - \left|L\right|]$ timestamps from $T$.
Function \calcMetric measures an indicator for the union of $\{l_k\}$ and a timestamp combination from $\{t_n\} \setminus \{l_k\}$. \paragraph{Optimal}
Function \evalSeq evaluates the result of \calcMetric by, e.g.,~estimating the standard deviation of all the distances from the previous/next {\thething}. Algorithm~\ref{algo:lmdk-sel-opt}, between Lines~{\ref{algo:lmdk-sel-opt-for-each}--\ref{algo:lmdk-sel-opt-end}} evaluates each option in \opts.
Function \getOpts returns all possible \emph{valid} sets of combinations \opt such that $\{l_{k+i}\} \subset \{l_{k+j}\}, \forall i, j \in [k, n] \mid i < j$, i.e.,~larger options must contain all of the timestamps that are present in smaller ones. It finds the option that is the most \emph{similar} to the original (Lines~{\ref{algo:lmdk-sel-opt-comparison}-\ref{algo:lmdk-sel-opt-end}}), i.e.,~the option that has an evaluation that differs the least from that of the sequence $T$ with {\thethings} $L$.
Each combination contains a set of timestamps with sizes $k + 1, k + 2, \dots, n$, where each one of them is a combination of $\{l_k\}$ with $x \in [1, n - k]$ timestamps from $\{t_n\}$.
\begin{algorithm} \begin{algorithm}
\caption{Optimal dummy {\thething} set options selection} \caption{Optimal dummy {\thething} set options generation}
\label{algo:lmdk-sel-opt} \label{algo:lmdk-sel-opt}
\DontPrintSemicolon \DontPrintSemicolon
\KwData{$\{t_n\}, \{l_k\}$} \KwData{$T, L$}
\SetKwInput{KwData}{Input} \SetKwInput{KwData}{Input}
@ -43,11 +40,10 @@ Each combination contains a set of timestamps with sizes $k + 1, k + 2, \dots, n
\BlankLine \BlankLine
% Evaluate the original % Evaluate the original
\metricOrig $\leftarrow$ \calcMetric{$\{t_n\}, \emptyset, \{l_k\}$}\; \evalOrig $\leftarrow$ \evalSeq{$T, \emptyset, L$}\;
\evalOrig $\leftarrow$ \evalSeq{\metricOrig}\;
% Get all possible option combinations % Get all possible option combinations
\opts $\leftarrow$ \getOpts{$\{t_n\}, \{l_k\}$}\; \opts $\leftarrow$ \getOpts{$T, L$}\;
% Track the minimum (best) evaluation % Track the minimum (best) evaluation
\diffMin $\leftarrow$ $\infty$\; \diffMin $\leftarrow$ $\infty$\;
@ -56,24 +52,28 @@ Each combination contains a set of timestamps with sizes $k + 1, k + 2, \dots, n
\optim $\leftarrow$ $[]$\; \optim $\leftarrow$ $[]$\;
\ForEach{\opt $\in$ \opts}{ \label{algo:lmdk-sel-opt-for-each} \ForEach{\opt $\in$ \opts}{ \label{algo:lmdk-sel-opt-for-each}
\evalSum $\leftarrow 0$\; \evalCur $\leftarrow 0$\;
\ForEach{\opti $\in$ \opt}{ \ForEach{\opti $\in$ \opt}{
\metricCur $\leftarrow$ \calcMetric{$\{t_n\}, \opti, \{l_k\}$}\;\label{algo:lmdk-sel-opt-comparison} \evalCur $\leftarrow$ \evalCur $+$ \evalSeq{$T, \opti, L$}/\#\opt\; \label{algo:lmdk-sel-opt-comparison}
\evalSum $\leftarrow$ \evalSum $+$ \evalSeq{\metricCur}\; }
% Compare with current optimal % Compare with current optimal
\diffCur $\leftarrow \left|\evalSum/\#\opt - \evalOrig\right|$\; \diffCur $\leftarrow \left|\evalCur - \evalOrig\right|$\;
\If{\diffCur $<$ \diffMin}{ \If{\diffCur $<$ \diffMin}{
\diffMin $\leftarrow$ \diffCur\; \diffMin $\leftarrow$ \diffCur\;
\optim $\leftarrow$ \opt\; \optim $\leftarrow$ \opt\;
} }
}
} \label{algo:lmdk-sel-opt-end} } \label{algo:lmdk-sel-opt-end}
\Return{\optim} \Return{\optim}
\end{algorithm} \end{algorithm}
Algorithm~\ref{algo:lmdk-sel-opt}, in particular, between Lines~{\ref{algo:lmdk-sel-opt-for-each}-\ref{algo:lmdk-sel-opt-end}} evaluates each option in \opts. Algorithm~\ref{algo:lmdk-sel-opt} guarantees to return the optimal set of dummy {\thethings} with regard to the original set $L$.
It finds the option that is the most \emph{similar} to the original (Lines~{\ref{algo:lmdk-sel-opt-comparison}-\ref{algo:lmdk-sel-opt-end}}), i.e.,~the option that has an evaluation that differs the least from that of the sequence $\{t_n\}$ with {\thethings} $\{l_k\}$. However, it is rather costly in terms of complexity: given $n$ regular events and a combination of size $r$, it requires $\mathcal{O}(C(n, r) + 2^C(n, r))$ time and $\mathcal{O}(r*C(n, r))$ space.
Next, we present a heuristic solution with improved time and space requirements.
\paragraph{Heuristic}
Algorithm~\ref{algo:lmdk-sel-heur}, follows an incremental methodology.
At each step it selects a new timestamp that corresponds to a regular ({non-\thething}) event from $T \setminus L$.
\begin{algorithm} \begin{algorithm}
\caption{Heuristic dummy {\thething} set options selection} \caption{Heuristic dummy {\thething} set options selection}
@ -81,30 +81,28 @@ It finds the option that is the most \emph{similar} to the original (Lines~{\ref
\DontPrintSemicolon \DontPrintSemicolon
\KwData{$\{t_n\}, \{l_k\}$} \KwData{$T, L$}
\KwResult{\optim} \KwResult{\optim}
\BlankLine \BlankLine
% Evaluate the original % Evaluate the original
\metricOrig $\leftarrow$ \calcMetric{$\{t_n\}, \emptyset, \{l_k\}$}\; \evalOrig $\leftarrow$ \evalSeq{$T, \emptyset, L$}\;
\evalOrig $\leftarrow$ \evalSeq{\metricOrig}\;
% Get all possible option combinations % Get all possible option combinations
\optim $\leftarrow$ $[]$\; \optim $\leftarrow$ $[]$\;
$\{l_{k'}\} \leftarrow \{l_k\}$\; $L' \leftarrow L$\;
\While{$\{l_{k'}\} \neq \{t_n\}$}{\label{algo:lmdk-sel-heur-while} \While{$L' \neq T$}{\label{algo:lmdk-sel-heur-while}
% Track the minimum (best) evaluation % Track the minimum (best) evaluation
\diffMin $\leftarrow$ $\infty$\; \diffMin $\leftarrow$ $\infty$\;
\optimi $\leftarrow$ $0$\; \optimi $\leftarrow$ Null\;
% Find the combinations for one more point % Find the combinations for one more point
\ForEach{\reg $\in \{t_n\} \setminus \{l_{k'}\}$}{ \ForEach{\reg $\in T \setminus L'$}{
% Evaluate current % Evaluate current
\metricCur $\leftarrow$ \calcMetric{$\{t_n\}, \reg, \{l_{k'}\}$}\;\label{algo:lmdk-sel-heur-comparison} \evalCur $\leftarrow$ \evalSeq{$T, \reg, L'$}\; \label{algo:lmdk-sel-heur-comparison}
\evalCur $\leftarrow$ \evalSeq{\metricCur}\;
% Compare evaluations % Compare evaluations
\diffCur $\leftarrow$ $\left|\evalCur - \evalOrig\right|$\; \diffCur $\leftarrow$ $\left|\evalCur - \evalOrig\right|$\;
@ -116,27 +114,31 @@ It finds the option that is the most \emph{similar} to the original (Lines~{\ref
} }
% Save new point to landmarks % Save new point to landmarks
$k' \leftarrow k' + 1$\; $L'.add(\optimi)$\;
$l_{k'} \leftarrow \optimi$\;
% Add new option % Add new option
\optim.add($\{l_{k'}\} \setminus \{l_k\}$)\; \optim.append($L' \setminus L$)\;
}\label{algo:lmdk-sel-heur-end} }\label{algo:lmdk-sel-heur-end}
\Return{\optim} \Return{\optim}
\end{algorithm} \end{algorithm}
Algorithm~\ref{algo:lmdk-sel-heur}, follows an incremental methodology.
At each step it selects a new timestamp that corresponds to a regular ({non-\thething}) event from $\{t_n\} \setminus \{l_k\}$.
Similar to Algorithm~\ref{algo:lmdk-sel-opt}, the selection is done based on a predefined metric (Lines~{\ref{algo:lmdk-sel-heur-comparison}-\ref{algo:lmdk-sel-heur-comparison-end}}). Similar to Algorithm~\ref{algo:lmdk-sel-opt}, the selection is done based on a predefined metric (Lines~{\ref{algo:lmdk-sel-heur-comparison}-\ref{algo:lmdk-sel-heur-comparison-end}}).
This process (Lines~{\ref{algo:lmdk-sel-heur-while}-\ref{algo:lmdk-sel-heur-end}}) goes on until we select a set that is equal to the size of the series of events, i.e.,~$\{l_{k'}\} = \{t_n\}$. This process (Lines~{\ref{algo:lmdk-sel-heur-while}-\ref{algo:lmdk-sel-heur-end}}) goes on until we select a set that is equal to the size of the series of events, i.e.,~$L' = T$.
Note that the reverse heuristic approach, i.e.,~starting with $\{t_n\}$ {\thethings} and removing until $\{l_k\}$, performs worse than and occasionally the same with Algorithm~\ref{algo:lmdk-sel-heur}. In terms of complexity: given $n$ regular events it requires $\mathcal{O}(n^2)$ time and space.
Note that the reverse heuristic approach, i.e.,~starting with $T$ {\thethings} and removing until $L$, performs similarly with Algorithm~\ref{algo:lmdk-sel-heur}.
\mk{WIP: Histograms}
\subsubsection{Privacy-preserving option selection} \subsubsection{Privacy-preserving option selection}
\label{subsec:lmdk-opt-sel} \label{subsec:lmdk-opt-sel}
\mk{WIP}
% Nearby events % Nearby events
Events that occur at recent timestamps are more likely to reveal sensitive information regarding the users involved~\cite{kellaris2014differentially}. Events that occur at recent timestamps are more likely to reveal sensitive information regarding the users involved~\cite{kellaris2014differentially}.
Thus, taking into account more recent events with respect to {\thethings} can result in less privacy loss and better privacy protection overall. Thus, taking into account more recent events with respect to {\thethings} can result in less privacy loss and better privacy protection overall.