statistical: Reviewed farokhi2020temporally
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@ -76,7 +76,10 @@
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\cite{li2007hiding} & & & & & (auto) & & \\ \hdashline
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\hyperlink{chen2017pegasus}{\emph{PeGaSus}} & infinite & streaming & global & event & linkage & perturbation & differential \\
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\cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\
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\cite{chen2017pegasus} & & & & & & (Laplace) & privacy \\ \hdashline
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\hyperlink{farokhi2020temporally}{Farokhi} & infinite & streaming & global & - & linkage & perturbation & differential \\
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\cite{farokhi2020temporally} & & & & & & (Laplace) & privacy \\
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\bottomrule
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@ -838,6 +838,7 @@
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publisher = {IEEE}
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}
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@article{jain2016big,
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title = {Big data privacy: a technological perspective and review},
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author = {Jain, Priyank and Gyanchandani, Manasi and Khare, Nilay},
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@ -849,7 +850,6 @@
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publisher = {Springer}
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}
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@article{ji2014differential,
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title = {Differential privacy and machine learning: a survey and review},
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author = {Ji, Zhanglong and Lipton, Zachary C and Elkan, Charles},
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@ -1267,6 +1267,15 @@
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publisher = {Now Publishers, Inc.}
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}
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@inproceedings{naim2019off,
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title = {ON-OFF privacy with correlated requests},
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author = {Naim, Carolina and Ye, Fangwei and El Rouayheb, Salim},
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booktitle = {2019 IEEE International Symposium on Information Theory (ISIT)},
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pages = {817--821},
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year = {2019},
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organization = {IEEE}
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}
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@inproceedings{narayanan2008robust,
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title = {Robust de-anonymization of large sparse data sets},
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author = {Narayanan, Arvind and Shmatikov, Vitaly},
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@ -1385,6 +1394,7 @@
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publisher = {Cambridge university press}
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}
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% new algorithm
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@misc{russell2018fitness,
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title = {Fitness app {Strava} exposes the location of military bases},
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author = {Russell, Jon},
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@ -1402,7 +1412,6 @@
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organization = {IEEE}
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}
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% new algorithm
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@article{satyanarayanan2017emergence,
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title = {The emergence of edge computing},
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author = {Satyanarayanan, Mahadev},
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@ -1674,41 +1683,6 @@
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publisher = {Springer}
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}
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@inproceedings{naim2019off,
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title={ON-OFF privacy with correlated requests},
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author={Naim, Carolina and Ye, Fangwei and El Rouayheb, Salim},
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booktitle={2019 IEEE International Symposium on Information Theory (ISIT)},
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pages={817--821},
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year={2019},
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organization={IEEE}
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}
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@inproceedings{ye2019preserving,
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title={Preserving ON-OFF privacy for past and future requests},
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author={Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
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booktitle={2019 IEEE Information Theory Workshop (ITW)},
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pages={1--5},
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year={2019},
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organization={IEEE}
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}
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@article{ye2020off,
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title={ON-OFF Privacy in the Presence of Correlation},
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author={Ye, Fangwei and Naim, Carolina and Rouayheb, Salim El},
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journal={arXiv preprint arXiv:2004.04186},
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year={2020}
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}
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@article{ye2021off,
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title={ON-OFF Privacy Against Correlation Over Time},
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author={Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
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journal={IEEE Transactions on Information Forensics and Security},
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volume={16},
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pages={2104--2117},
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year={2021},
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publisher={IEEE}
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}
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@article{warner1965randomized,
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title = {Randomized response: A survey technique for eliminating evasive answer bias},
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author = {Warner, Stanley L},
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@ -1844,6 +1818,32 @@
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organization = {IEEE}
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}
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@inproceedings{ye2019preserving,
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title = {Preserving ON-OFF privacy for past and future requests},
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author = {Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
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booktitle = {2019 IEEE Information Theory Workshop (ITW)},
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pages = {1--5},
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year = {2019},
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organization = {IEEE}
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}
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@article{ye2020off,
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title = {ON-OFF Privacy in the Presence of Correlation},
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author = {Ye, Fangwei and Naim, Carolina and Rouayheb, Salim El},
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journal = {arXiv preprint arXiv:2004.04186},
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year = {2020}
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}
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@article{ye2021off,
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title = {ON-OFF Privacy Against Correlation Over Time},
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author = {Ye, Fangwei and Naim, Carolina and El Rouayheb, Salim},
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journal = {IEEE Transactions on Information Forensics and Security},
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volume = {16},
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pages = {2104--2117},
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year = {2021},
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publisher = {IEEE}
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}
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@inproceedings{yuan2010t,
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title = {T-drive: driving directions based on taxi trajectories},
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author = {Yuan, Jing and Zheng, Yu and Zhang, Chengyang and Xie, Wenlei and Xie, Xing and Sun, Guangzhong and Huang, Yan},
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@ -354,3 +354,21 @@ The Perturber consumes the incoming data stream, adds noise $\varepsilon_p$, whi
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The data-adaptive Grouper consumes the original stream and partitions the data into well-approximated regions using, also part of the available privacy budget, $\varepsilon_g$.
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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.
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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.
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% Temporally Discounted Differential Privacy for Evolving Datasets on an Infinite Horizon
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% - statistical
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% - infinite
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% - streaming
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% - linkage
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% - -
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% - differential privacy
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% - perturbation (Laplace)
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\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.
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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.
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These weights decrease the further that we observe in the past.
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The author implements an exponentially and a hyperbolic discounted scheme.
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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.
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Increasing the discount factor offers stronger privacy protection, equivalent to that of user-level.
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Whereas, increasing the discount coefficient resembles the behavior of event-level differential privacy.
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Selecting a suitable value for the privacy budget and the discount parameter allows for bounding the overall privacy loss in an infinite observation scenario.
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The assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios.
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