statistical: Reviewed farokhi2020temporally

tables/statistical.tex

@@ -76,7 +76,10 @@
       \cite{li2007hiding} & & & & & (auto) & & \\ \hdashline
 
       \hyperlink{chen2017pegasus}{\emph{PeGaSus}} & infinite & streaming & global & event & linkage & perturbation & differential \\
-      \cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\
+      \cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\ \hdashline
+
+      \hyperlink{farokhi2020temporally}{Farokhi} & infinite & streaming & global & - & linkage & perturbation & differential \\
+      \cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\
 
       \bottomrule
 

text/bibliography.bib

@@ -838,6 +838,7 @@
   publisher = {IEEE}
 }
 
+
 @article{jain2016big,
   title     = {Big data privacy: a technological perspective and review},
   author    = {Jain, Priyank and Gyanchandani, Manasi and Khare, Nilay},
@@ -849,7 +850,6 @@
   publisher = {Springer}
 }
 
-
 @article{ji2014differential,
   title   = {Differential privacy and machine learning: a survey and review},
   author  = {Ji, Zhanglong and Lipton, Zachary C and Elkan, Charles},
@@ -1267,6 +1267,15 @@
   publisher = {Now Publishers, Inc.}
 }
 
+@inproceedings{naim2019off,
+  title        = {ON-OFF privacy with correlated requests},
+  author       = {Naim, Carolina and Ye, Fangwei and El Rouayheb, Salim},
+  booktitle    = {2019 IEEE International Symposium on Information Theory (ISIT)},
+  pages        = {817--821},
+  year         = {2019},
+  organization = {IEEE}
+}
+
 @inproceedings{narayanan2008robust,
   title        = {Robust de-anonymization of large sparse data sets},
   author       = {Narayanan, Arvind and Shmatikov, Vitaly},
@@ -1385,6 +1394,7 @@
   publisher = {Cambridge university press}
 }
 
+%  new algorithm
 @misc{russell2018fitness,
   title        = {Fitness app {Strava} exposes the location of military bases},
   author       = {Russell, Jon},
@@ -1402,7 +1412,6 @@
   organization = {IEEE}
 }
 
-%  new algorithm
 @article{satyanarayanan2017emergence,
   title     = {The emergence of edge computing},
   author    = {Satyanarayanan, Mahadev},
@@ -1674,41 +1683,6 @@
   publisher = {Springer}
 }
 
-@inproceedings{naim2019off,
-  title={ON-OFF privacy with correlated requests},
-  author={Naim, Carolina and Ye, Fangwei and El Rouayheb, Salim},
-  booktitle={2019 IEEE International Symposium on Information Theory (ISIT)},
-  pages={817--821},
-  year={2019},
-  organization={IEEE}
-}
-
-@inproceedings{ye2019preserving,
-  title={Preserving ON-OFF privacy for past and future requests},
-  author={Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
-  booktitle={2019 IEEE Information Theory Workshop (ITW)},
-  pages={1--5},
-  year={2019},
-  organization={IEEE}
-}
-
-@article{ye2020off,
-  title={ON-OFF Privacy in the Presence of Correlation},
-  author={Ye, Fangwei and Naim, Carolina and Rouayheb, Salim El},
-  journal={arXiv preprint arXiv:2004.04186},
-  year={2020}
-}
-
-@article{ye2021off,
-  title={ON-OFF Privacy Against Correlation Over Time},
-  author={Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
-  journal={IEEE Transactions on Information Forensics and Security},
-  volume={16},
-  pages={2104--2117},
-  year={2021},
-  publisher={IEEE}
-}
-
 @article{warner1965randomized,
   title     = {Randomized response: A survey technique for eliminating evasive answer bias},
   author    = {Warner, Stanley L},
@@ -1844,6 +1818,32 @@
   organization = {IEEE}
 }
 
+@inproceedings{ye2019preserving,
+  title        = {Preserving ON-OFF privacy for past and future requests},
+  author       = {Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
+  booktitle    = {2019 IEEE Information Theory Workshop (ITW)},
+  pages        = {1--5},
+  year         = {2019},
+  organization = {IEEE}
+}
+
+@article{ye2020off,
+  title   = {ON-OFF Privacy in the Presence of Correlation},
+  author  = {Ye, Fangwei and Naim, Carolina and Rouayheb, Salim El},
+  journal = {arXiv preprint arXiv:2004.04186},
+  year    = {2020}
+}
+
+@article{ye2021off,
+  title     = {ON-OFF Privacy Against Correlation Over Time},
+  author    = {Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
+  journal   = {IEEE Transactions on Information Forensics and Security},
+  volume    = {16},
+  pages     = {2104--2117},
+  year      = {2021},
+  publisher = {IEEE}
+}
+
 @inproceedings{yuan2010t,
   title     = {T-drive: driving directions based on taxi trajectories},
   author    = {Yuan, Jing and Zheng, Yu and Zhang, Chengyang and Xie, Wenlei and Xie, Xing and Sun, Guangzhong and Huang, Yan},

text/related/statistical.tex

@@ -354,3 +354,21 @@ The Perturber consumes the incoming data stream, adds noise $\varepsilon_p$, whi
 The data-adaptive Grouper consumes the original stream and partitions the data into well-approximated regions using, also part of the available privacy budget, $\varepsilon_g$.
 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 the economics literature.
+More specifically, at each timestamp, the scheme of 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.
+The assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios.