the-thing: Labels

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Manos Katsomallos 2021-07-19 11:11:51 +02:00
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\section{Evaluation} \section{Evaluation}
\label{sec:the-thing-eval} \label{sec:lmdk-eval}
In this section we present the experiments that we performed on real and synthetic data sets. In this section we present the experiments that we performed on real and synthetic data sets.
With the experiments on the synthetic data sets we show the privacy loss by our framework when tuning the size and statistical characteristics of the input {\thething} set $L$. With the experiments on the synthetic data sets we show the privacy loss by our framework when tuning the size and statistical characteristics of the input {\thething} set $L$.

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\section{{\Thething} privacy} \section{{\Thething} privacy}
\label{sec:prob} \label{sec:lmdk-prob}
{\Thething} privacy is based on differential privacy. {\Thething} privacy is based on differential privacy.
For this reason, we revisit the definition and important properties of differential privacy before moving on to the main ideas of this paper. For this reason, we revisit the definition and important properties of differential privacy before moving on to the main ideas of this paper.