From f19a16df5ee18a79dad088c8e4af19c501b746cf Mon Sep 17 00:00:00 2001 From: Manos Katsomallos Date: Tue, 7 Sep 2021 15:16:37 +0300 Subject: [PATCH] Reviewed ou2018optimal --- text/related/micro.tex | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/text/related/micro.tex b/text/related/micro.tex index 8c3704d..ffe1d4e 100644 --- a/text/related/micro.tex +++ b/text/related/micro.tex @@ -274,6 +274,22 @@ private and eligible for publishing. The authors compare their design with that of~\cite{chen2012differentially} and~\cite{he2015dpt} by running several tests, and ascertain that it outperforms them in terms of data utility. However, due to DP-Star's privacy budget distribution to its different phases, for small values of $\varepsilon$ the framework's privacy performance is inferior to that of its competitors. +% An Optimal Pufferfish Privacy Mechanism for Temporally Correlated Trajectories +% - microdata +% - finite (sequential) +% - batch +% - dependence (temporal) +% - local +% - event +% - differential privacy +% - perturbation (randomized response, Laplace) +\hypertarget{ou2018optimal}{Ou et al.}~\cite{ou2018optimal} designed \emph{FGS-Pufferfish} for publishing temporally correlated trajectory data while protecting temporal correlation. +FGS-Pufferfish transforms a user's daily trajectories into a set of sine and cosine waves of different frequencies along with the corresponding Fourier coefficients. +Then, it adds Laplace noise to the Fourier coefficients' geometric sum. +The authors obtain the optimal noisy Fourier coefficients by solving the constrained optimization problem via the Lagrange Multiplier method depending on the available privacy budget. +They evaluate both the location data utility and the temporal correlation utility. +The experimental evaluation shows that FGS-Pufferfish outperforms CTS-DP~\cite{wang2017cts} in terms of the trade-off between privacy and location utility. + \subsection{Infinite observation} \label{subsec:micro-infinite} @@ -422,7 +438,7 @@ Last but not least, the technique requires the calculation of the temporal priva % ON-OFF Privacy in the Presence of Correlation % ON-OFF Privacy Against Correlation Over Time % - microdata -% - infinite +% - infinite (sequential) % - streaming % - dependence % - event