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abstract: Review

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Manos Katsomallos 2021-07-18 19:38:03 +02:00
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@ -18,8 +18,8 @@ We provide an insight into time-related properties of the algorithms, e.g.,~if t
In the second part, we argue that in continuous data publishing, events are not equally significant in terms of privacy, and hence they should affect the privacy-preserving processing. In the second part, we argue that in continuous data publishing, events are not equally significant in terms of privacy, and hence they should affect the privacy-preserving processing.
Differential privacy is a well-established paradigm in privacy-preserving time series publishing. Differential privacy is a well-established paradigm in privacy-preserving time series publishing.
Different schemes exist, protecting either a single timestamp, or all the data per user or per window in the time series, considering however all timestamps as equally significant. Different schemes exist, protecting either a single timestamp, or all the data per user or per window in the time series, considering however all timestamps as equally significant.
In this work, we propose a novel configurable privacy scheme, \emph{\thething} privacy, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly. In this part, we propose a novel configurable privacy scheme, \emph{\thething} privacy, which takes into account significant events (\emph{\thethings}) in the time series and allocates the available privacy budget accordingly.
We design two privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets. We design three privacy models that guarantee {\thething} privacy and validate our proposal on real and synthetic data sets.
\paragraph{Keywords:} \paragraph{Keywords:}