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statistical: Reviewed farokhi2020temporally

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Manos Katsomallos
2021-09-03 04:40:41 +03:00
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@ -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.