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related: Reordered the works in statistical

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Manos Katsomallos 2021-09-06 17:15:49 +03:00
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8
tables/statistical.tex
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@ -78,14 +78,14 @@
\hyperlink{chen2017pegasus}{\emph{PeGaSus}} & infinite & streaming & global & event & linkage & perturbation & differential \\
\cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\ \hdashline
\hyperlink{farokhi2020temporally}{Farokhi} & infinite & streaming & global & - & linkage & perturbation & differential \\
\cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\ \hdashline
\hyperlink{wang2018privacy}{\textbf{\emph{DP-PSP}}} & infinite & streaming & global & $w$-event & linkage & perturbation & differential \\
\cite{wang2018privacy} & & & & & & (Laplace) & privacy \\
\hyperlink{ma2019real}{\textbf{\emph{RPTR}}} & infinite & streaming & global & $w$-event & linkage & perturbation & differential \\
\cite{ma2019real} & & & & & & (Laplace) & privacy \\ \hdashline
\hyperlink{wang2018privacy}{\textbf{\emph{DP-PSP}}} & infinite & streaming & global & $w$-event & linkage & perturbation & differential \\
\cite{wang2018privacy} & & & & & & (Laplace) & privacy \\
\hyperlink{farokhi2020temporally}{Farokhi} & infinite & streaming & global & - & linkage & perturbation & differential \\
\cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\ \hdashline
\bottomrule

70
text/related/statistical.tex
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@ -355,41 +355,6 @@ The data-adaptive Grouper consumes the original stream and partitions the data i
Finally, a query specific Smoother combines the independent information produced by the Perturber and the Grouper, and performs post-processing by calculating the final estimates of the Perturber's values for each partition created by the Grouper at each timestamp.
The combination of the Perturber and the Grouper follows the sequential composition and post-processing properties of differential privacy, thus, the resulting algorithm satisfies ($\varepsilon_p + \varepsilon_g$)-differential privacy.
% Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon
% - statistical
% - infinite
% - streaming
% - linkage
% - -
% - differential privacy
% - perturbation (Laplace)
\hypertarget{farokhi2020temporally}{Farokhi}~\cite{farokhi2020temporally} proposed a relaxation of the user-level protection of differential privacy based on the discounted utility theory in economics.
More specifically, at each timestamp, the scheme of \emph{temporally discounted differential privacy} assigns different weights to the privacy budgets that have been invested in previous timestamps.
These weights decrease the further that we observe in the past.
The author implements an exponentially and a hyperbolic discounted scheme.
In the former, the discount factor, which is positive and less than $1$, and in the latter, the discounting coefficient, which is greater or equal to $0$, allows the adjustment of temporal discounting.
Increasing the discount factor offers stronger privacy protection, equivalent to that of user-level.
Whereas, increasing the discount coefficient resembles the behavior of event-level differential privacy.
Selecting a suitable value for the privacy budget and the discount parameter allows for bounding the overall privacy loss in an infinite observation scenario.
However, the assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios for statistical data.
% Real-Time Privacy-Preserving Data Release Over Vehicle Trajectory
% - statistical
% - infinite
% - streaming
% - linkage
% - global
% - w-event
% - differential privacy
% - perturbation (Laplace)
\hypertarget{ma2019real}{Ma et al.}~\cite{ma2019real} implemented \emph{RPTR}, a $w$-event differential privacy mechanism for protecting statistics of vehicular trajectory data in real time.
RPTR adapts the rate with which it samples data according to the accuracy with which it can predict future statistics based on historical data and position transfer probability matrix and according to how much the original data change through time based on Pearson coefficient.
Before releasing data statistics, the mechanism perturbs the original values with Laplacian noise the impact of which is mitigated by using Ensemble Kalman filtering.
The combination of adaptive sampling and filtering can improve the accuracy when predicting the values of non-sampled data points, and thus saving more privacy budget (i.e.,~higher data utility) for data points that the mechanism decides to release.
The mechanism detects highly frequented map regions and, using a quad-tree, it calculate the each region's privacy weight.
In their implementation, the authors assume that highly frequented regions tend to be more privacy sensitive, and thus more noise (i.e.,~less privacy budget to invest) needs to be introduced before publicly releasing the users' data falling into these regions.
The efficiency (both in terms of user privacy and data utility) of the mechanism depends on the number of regions that it divides the map, and therefore the challenge of its optimal division is an interesting future research topic.
% Privacy-protected statistics publication over social media user trajectory streams
% - statistical
% - infinite
@ -408,3 +373,38 @@ Thus, at some timestamps, they can predict accurately the upcoming statistics, a
DP-PSP allocates the available privacy budget, in an exponentially decaying fashion, in a sliding window with a user-defined size $w$, satisfying $w$-event-level privacy.
Statistics over the trajectory combined with Laplacian noise are released in the end of the process by DP-PSP.
From the implementation, it is not clear how DP-PSP takes into consideration all of the user preferences regarding the size of $w$ while releasing statistics of the data of all of the sample.
% Real-Time Privacy-Preserving Data Release Over Vehicle Trajectory
% - statistical
% - infinite
% - streaming
% - linkage
% - global
% - w-event
% - differential privacy
% - perturbation (Laplace)
\hypertarget{ma2019real}{Ma et al.}~\cite{ma2019real} implemented \emph{RPTR}, a $w$-event differential privacy mechanism for protecting statistics of vehicular trajectory data in real time.
RPTR adapts the rate with which it samples data according to the accuracy with which it can predict future statistics based on historical data and position transfer probability matrix and according to how much the original data change through time based on Pearson coefficient.
Before releasing data statistics, the mechanism perturbs the original values with Laplacian noise the impact of which is mitigated by using Ensemble Kalman filtering.
The combination of adaptive sampling and filtering can improve the accuracy when predicting the values of non-sampled data points, and thus saving more privacy budget (i.e.,~higher data utility) for data points that the mechanism decides to release.
The mechanism detects highly frequented map regions and, using a quad-tree, it calculate the each region's privacy weight.
In their implementation, the authors assume that highly frequented regions tend to be more privacy sensitive, and thus more noise (i.e.,~less privacy budget to invest) needs to be introduced before publicly releasing the users' data falling into these regions.
The efficiency (both in terms of user privacy and data utility) of the mechanism depends on the number of regions that it divides the map, and therefore the challenge of its optimal division is an interesting future research topic.
% Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon
% - statistical
% - infinite
% - streaming
% - linkage
% - -
% - differential privacy
% - perturbation (Laplace)
\hypertarget{farokhi2020temporally}{Farokhi}~\cite{farokhi2020temporally} proposed a relaxation of the user-level protection of differential privacy based on the discounted utility theory in economics.
More specifically, at each timestamp, the scheme of \emph{temporally discounted differential privacy} assigns different weights to the privacy budgets that have been invested in previous timestamps.
These weights decrease the further that we observe in the past.
The author implements an exponentially and a hyperbolic discounted scheme.
In the former, the discount factor, which is positive and less than $1$, and in the latter, the discounting coefficient, which is greater or equal to $0$, allows the adjustment of temporal discounting.
Increasing the discount factor offers stronger privacy protection, equivalent to that of user-level.
Whereas, increasing the discount coefficient resembles the behavior of event-level differential privacy.
Selecting a suitable value for the privacy budget and the discount parameter allows for bounding the overall privacy loss in an infinite observation scenario.
However, the assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios for statistical data.