Reviewed ou2018optimal

tables/micro.tex

@@ -44,7 +44,10 @@
       \cite{primault2015time} & (sequential) & & & & & & \\ \hdashline
 
       \hyperlink{gursoy2018differentially}{\textbf{\emph{DP-Star}}} & finite & batch & global & user & linkage & perturbation & differential \\
-      \cite{gursoy2018differentially} & (sequential) & & & & & (Laplace) & privacy \\
+      \cite{gursoy2018differentially} & (sequential) & & & & & (Laplace) & privacy \\ \hdashline
+
+      \hyperlink{ou2018optimal}{\textbf{\emph{FGS-Pufferfish}}} & finite & batch & local & event & dependence & perturbation & differential \\
+      \cite{ou2018optimal} & (sequential) & & & & (temporal) & (Laplace) & privacy \\
 
       \midrule
 

text/bibliography.bib

@@ -1310,6 +1310,16 @@
            Accessed on November 11, 2020}
 }
 
+@article{ou2018optimal,
+  title     = {An optimal pufferfish privacy mechanism for temporally correlated trajectories},
+  author    = {Ou, Lu and Qin, Zheng and Liao, Shaolin and Yin, Hui and Jia, Xiaohua},
+  journal   = {IEEE Access},
+  volume    = {6},
+  pages     = {37150--37165},
+  year      = {2018},
+  publisher = {IEEE}
+}
+
 @inproceedings{ozccep2015stream,
   title        = {Stream-query compilation with ontologies},
   author       = {{\"O}z{\c{c}}ep, {\"O}zg{\"u}r L{\"u}tf{\"u} and M{\"o}ller, Ralf and Neuenstadt, Christian},

text/main.tex

@@ -98,110 +98,6 @@
 \input{theotherthing/main}
 \input{conclusion/main}
 
-% 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}
-
-\emph{FGS-Pufferfish}
-
-temporally correlated trajectory data
-
-
-First, a Laplace noise mechanism is realized through geometric sum of noisy Fourier coefficients of temporally correlated daily trajectories. 
-
-We achieve better data utility for a given privacy budget by solving a constrained optimization problem of the noisy Fourier coefficients via the Lagrange multiplier method.
-
-
-generation of Laplace noise via the Fourier coefficients' geometric
-
-
-We present an analytical formula of the optimized Fourier coefficients noise for the constrained optimization problem of achieving a better data utility for a given privacy budget. 
-
-We provide theoretical analysis of the data utility and privacy, as well as the posterior-to-prior knowledge gain of an adversary.
-
-
-
-
-
-we quantify the temporal correlation in a rigorous mathematics way. 
-
-by adding noise to the Fourier coefficients through geometric sum. 
-
-The discrete Fourier transform transforms a user's daily trajectory
-
-into a set of sine and cosine waves
-of different frequencies and corresponding Fourier coefficients 
-
-In this paper, we assume that both the real part and the imaginary part of the Fourier coefficient follow the same Gaussian distribution 
-
-
-
-The constrained optimization problem is a strategy of finding the local extrema (maxima and minima) of a function f (b) subject to equality constraint g(b) = 0
-
-
-The user's mobility pattern can be described by the conditional probability of the next i-th location from the current n-th location
-
-
-A user's temporal correlation depicts the relation
-between two locations at current time slot tn and its following i-th time slots tn+i
-
-
-the Pufferfish secrets set consists of temporal correlations of all users
-
-A correlation secrets pair consists of two temporal correlations of any two users in the same database
-
-the Fourier coefficients of the temporal correlation are closely related to those of the daily trajectory. 
-Thus it is natural to add noise to the Fourier coefficients of the trajectory
-
-
-we propose to optimize the Fourier coefficients noise for the problem of temporal correlation privacy.
-
-First, we add the noise in the Fourier coefficients
-
-the noisy temporal correlation is obtained from the noisy daily trajectory
-
-
-we propose to achieve the optimal data utility for a given privacy budget
-
-Here we consider two utilities, including the location utility and the correlation utility
-
-For the location utility, we want the average noisy location deviates from its raw location as small as possible
-
-The correlation utility is the average of the deviation of the noisy correlation from its raw value
-
-
-
-
-Our goal is to prevent an adversary from mining a user's privacy through analyzing the user's temporal correlation based on the adversary’s prior knowledge about the user.
-
-
-
-First, we define the constrained optimization problem of achieving a better data utility for a given privacy budget given by the Laplace scale parameter
-Next, we solve the constrained optimization problem via the LM method and obtain the optimal obtained Laplace scale parameter for the noisy Fourier coefficients
-Then, the FGS-Pufferfish privacy mechanism adds noise to the Fourier coefficients and obtain the noisy Fourier coefficients
-At last, we obtain the sanitized daily trajectories with the noisy Fourier coefficients 
-
-
-we design an algorithm to release temporally correlated trajectories in order to protect individuals' privacy. 
-Because our goal is to protect the temporal correlation of a user’s daily trajectory, we first calculate the Fourier coefficients of a daily trajectory which is related to the Fourier coefficients of its temporal correlation.
-Then, to achieve the Laplace distribution of the noisy temporal correlation through the Fourier coefficients noise mechanism, i.e., adding noise to Fourier coefficients, with following the geometric distribution.
-Furthermore, we obtain the optimal Laplace scale parameters for the noisy Fourier coefficients. Finally, we use IDFT to obtain the noisy loca- tions of the sanitized daily trajectory
-
-
-We propose a Laplace noise mechanism based on the noisy Fourier coefficients' geometric sum, satisfying Pufferfish privacy, i.e., the FGS-Pufferfish privacy mechanism, to protect the temporal correlation of a user's daily trajectories. 
-The optimal noisy Fourier coefficients are obtained by solving the constrained optimization problem via the LM method to achieves a better data utility for a given privacy budget. 
-Experiments with both simulated and real-life data show that our FGS-Pufferfish privacy mechanism achieves better data utility and privacy compared to the existing approach.
-Although we only deal with daily trajectories with a constant time interval, our proposed mechanism can be readily modified for time-series data with irregular time intervals
-
-
-
 \backmatter
 
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