50 lines
3.6 KiB
TeX
50 lines
3.6 KiB
TeX
\section{Contribution}
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\label{sec:contr}
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Our objective is to study the problems around the subject of two contrasting desiderata: quality and privacy in user-generated Big Data.
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We consider the scenario where data are processed/published in a continuous manner and envision a configurable privacy technique that we can tune depending on the use case requirements.
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The main challenge in this setting is maintaining a balance between privacy and utility.
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Furthermore, we consider the presence of temporal correlation, which is inherent in continuous data publishing schemes and can lead to additional privacy loss.
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\paragraph{Privacy, space and time}
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The first contribution of this thesis is the survey~\cite{katsomallos2019privacy}
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% \kat{cite it here}
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of the existing literature regarding methods on privacy-preserving continuous data publishing, which appeared in
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% \kat{name the journal and the special issue}
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the special feature on Geospatial Privacy and Security in the $19$th Journal of Spatial Information Science.
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We study works that were published over the past two decades and provide a guide that will navigate its users through the available methodology and help them select the algorithms that are fitting best their needs.
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We categorize the works that we review depending on if they deal with \emph{microdata} or \emph{statistical data}.
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Then, we group them based on the duration of the processing/publishing that they aim for.
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Furthermore, we document in detail the privacy protection characteristics of each reviewed method.
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\paragraph{{\Thething} privacy}
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Our second contribution is the proposal and formal definition of a novel privacy notion, \emph{{\thething} privacy}.
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Contrary to the existing privacy protection levels, our notion differentiates events between regular and events that a user might consider more privacy-sensitive, i.e.~\emph{{\thethings}}.
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The introduction of {\thethings}, allows for a configurable privacy protection.
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First, we design and implement three {\thething} privacy schemes, accounting for {\thethings} spanning a finite time series.
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Thereafter, we investigate {\thething} privacy under temporal correlation, which is inherent in time series publishing, and study how {\thethings} can affect the propagation of temporal privacy loss.
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\paragraph{Dummy {\thething} selection}
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The third contribution of this thesis is the design of a module that extends our {\thething} privacy schemes and provides additional protection to {\thethings}.
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In other words, we answer the question \emph{`How can we protect the fact that we care more about certain events?'}.
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We design an additional differential privacy mechanism, based on the exponential mechanism, that we can easily plug in to the proposed {\thething} privacy schemes.
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We provide an optimal solution to this problem, which we improve by adopting a heuristic approach, and then implement a more efficient module that relies on partitioning.
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\bigskip
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We extensively evaluate the methods that we propose by conducting experiments on real and synthetic data sets.
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We compare {\thething} privacy with event- and user-level privacy protection, and investigate the behavior of the overall privacy loss under temporal correlation for different distributions of {\thethings}.
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Furthermore, we estimate the impact of the privacy-preserving dummy {\thething} selection module on the utility of our privacy scheme.
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The second and the third contributions are described in the article~\cite{katsomallos2022landmark},
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% \kat{cite the technical report}
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which is submitted at the research papers track
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% \kat{name the conference}
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of the $12$th ACM Conference on Data and Application Security and Privacy.
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