evaluation: Minor corrections and text
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@ -20,7 +20,15 @@ def main(args):
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# Distribution type
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dist_type = np.array(range(0, 4))
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# Number of landmarks
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lmdk_n = np.array(range(int(.2*args.time), args.time, int(args.time/5)))
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lmdk_n = np.array(range(0, args.time + 1, int(args.time/5)))
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markers = [
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'^', # Symmetric
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'v', # Skewed
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'D', # Bimodal
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's' # Uniform
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]
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# Initialize plot
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lmdk_lib.plot_init()
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# Width of bars
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@ -30,11 +38,13 @@ def main(args):
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x_margin = bar_width*(len(dist_type)/2 + 1)
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plt.xticks(x_i, ((lmdk_n/len(seq))*100).astype(int))
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plt.xlabel('Landmarks (%)') # Set x axis label.
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plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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# plt.xlim(x_i.min() - x_margin, x_i.max() + x_margin)
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plt.xlim(x_i.min(), x_i.max())
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# The y axis
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# plt.yscale('log')
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plt.ylabel('Euclidean distance') # Set y axis label.
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# plt.ylabel('Wasserstein distance') # Set y axis label.
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plt.ylim(0, 1)
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plt.ylabel('Normalized Euclidean distance') # Set y axis label.
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# plt.ylabel('Normalized Wasserstein distance') # Set y axis label.
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# Bar offset
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x_offset = -(bar_width/2)*(len(dist_type) - 1)
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for d_i, d in enumerate(dist_type):
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@ -47,27 +57,41 @@ def main(args):
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print('(%d/%d) %s... ' %(d_i + 1, len(dist_type), title), end='', flush=True)
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mae = np.zeros(len(lmdk_n))
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for n_i, n in enumerate(lmdk_n):
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for r in range(args.reps):
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if n == lmdk_n[-1]:
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break
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for r in range(args.iter):
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lmdks = lmdk_lib.get_lmdks(seq, n, d)
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hist, h = lmdk_lib.get_hist(seq, lmdks)
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opts = lmdk_sel.get_opts_from_top_h(seq, lmdks)
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delta = 1.0
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res, _ = exp_mech.exponential(hist, opts, exp_mech.score, delta, epsilon)
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mae[n_i] += lmdk_lib.get_norm(hist, res)/args.reps # Euclidean
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# mae[n_i] += lmdk_lib.get_emd(hist, res)/args.reps # Wasserstein
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mae[n_i] += lmdk_lib.get_norm(hist, res)/args.iter # Euclidean
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# mae[n_i] += lmdk_lib.get_emd(hist, res)/args.iter # Wasserstein
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mae = mae/21 # Euclidean
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# mae = mae/11.75 # Wasserstein
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print('[OK]', flush=True)
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# Plot bar for current distribution
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plt.bar(
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x_i + x_offset,
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# # Plot bar for current distribution
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# plt.bar(
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# x_i + x_offset,
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# mae,
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# bar_width,
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# label=label,
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# linewidth=lmdk_lib.line_width
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# )
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# # Change offset for next bar
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# x_offset += bar_width
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# Plot line
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plt.plot(
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x_i,
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mae,
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bar_width,
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label=label,
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marker=markers[d_i],
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markersize=lmdk_lib.marker_size,
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markeredgewidth=0,
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linewidth=lmdk_lib.line_width
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)
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# Change offset for next bar
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x_offset += bar_width
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path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-norm')
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# path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-emd')
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path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-norm-l')
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# path = str('../../rslt/lmdk_sel_cmp/' + 'lmdk_sel_cmp-emd-l')
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# Plot legend
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lmdk_lib.plot_legend()
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# Show plot
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@ -81,7 +105,7 @@ def main(args):
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Parse arguments.
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Optional:
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reps - The number of repetitions.
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iter - The number of iterations.
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time - The time limit of the sequence.
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'''
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def parse_args():
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@ -91,7 +115,7 @@ def parse_args():
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# Mandatory arguments.
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# Optional arguments.
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parser.add_argument('-r', '--reps', help='The number of repetitions.', type=int, default=1)
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parser.add_argument('-i', '--iter', help='The number of iterations.', type=int, default=1)
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parser.add_argument('-t', '--time', help='The time limit of the sequence.', type=int, default=100)
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# Parse arguments.
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rslt/lmdk_sel_cmp/lmdk_sel_cmp-norm-l.pdf
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@ -1,9 +1,36 @@
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\section{Selection of events}
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\label{sec:lmdk-sel-eval}
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In this section we present the experiments that we performed, to test the methodology that we presented in Section~\ref{subsec:lmdk-sel-sol}, on real and synthetic data sets.
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% With the experiments on the real data sets (Section~\ref{subsec:lmdk-expt-bgt}), we show the performance in terms of utility of our three {\thething} mechanisms.
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% With the experiments on the synthetic data sets (Section~\ref{subsec:lmdk-expt-cor}) we show the privacy loss by our framework when tuning the size and statistical characteristics of the input {\thething} set $L$ with special emphasis on how the privacy loss under temporal correlation is affected by the number and distribution of the {\thethings}.
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In this section we present the experiments that we performed, to test the methodology that we presented in Section~\ref{subsec:lmdk-sel-sol}, on real and synthetic data sets.
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With the experiments on the synthetic data sets (Section~\ref{subsec:sel-utiliy}) we show the normaziled distances for various {\thething} percentages.
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privacy loss by our framework when tuning the size and statistical characteristics of the input {\thething} set $L$ with special emphasis on how the privacy loss under temporal correlation is affected by the number and distribution of the {\thethings}.
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With the experiments on the real data sets (Section~\ref{subsec:sel-prv}), we show the performance in terms of utility of our three {\thething} mechanisms in combination with privacy preserving {\thething} that can be possibly applied to humans.
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\subsection{{\Thething} selection utility metrics}
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\label{subsec:sel-utl}
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Figure~\ref{fig:sel-dist} demonstrates the normalized distance that we obtain when we utilize either (a)~the Euclidean or (b)~the Wasserstein distance metric to obtain a set of {\thethings} including regular events.
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\begin{figure}[htp]
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\centering
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\subcaptionbox{Euclidean\label{fig:sel-dist-norm}}{%
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\includegraphics[width=.5\linewidth]{evaluation/sel-dist-norm}%
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}%
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\subcaptionbox{Wasserstein\label{fig:sel-dist-emd}}{%
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\includegraphics[width=.5\linewidth]{evaluation/sel-dist-emd}%
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}%
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\caption{The normalized (a)~Euclidean, and (b)~Wasserstein distance of the generated {\thething} sets for different {\thething} percentages.}
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\label{fig:sel-dist}
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\end{figure}
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Comparing the results of the Euclidean distance in Figure~\ref{fig:sel-dist-norm} with those of the Wasserstein in Figure~\ref{fig:sel-dist-emd} we conclude that the Euclidean distance provides more consistent results for all possible distributions.
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The maximum difference is approximately $0.4$ for the former and $0.7$ for the latter between the bimodal and skewed {\thething} distribution.
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Therefore, we choose to utilize the Euclidean distance metric for the implementation of the privacy-preserving {\thething} selection.
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\subsection{Budget allocation and {\thething} selection}
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\label{subsec:sel-prv}
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Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptive (see Section~\ref{subsec:lmdk-mechs}) in combination with the {\thething} selection component.
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@ -19,7 +46,7 @@ Figure~\ref{fig:real-sel} exhibits the performance of Skip, Uniform, and Adaptiv
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\subcaptionbox{T-drive\label{fig:t-drive-sel}}{%
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\includegraphics[width=.5\linewidth]{evaluation/t-drive-sel}%
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}%
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\caption{The mean absolute error (a)~as a percentage, (b)~in kWh, and (c)~in meters of the released data for different {\thethings} percentages.}
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\caption{The mean absolute error (a)~as a percentage, (b)~in kWh, and (c)~in meters of the released data for different {\thething} percentages.}
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\label{fig:real-sel}
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\end{figure}
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