manos/the-last-thing
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

statistical: Revision of farokhi2020temporally

Browse Source
This commit is contained in:
Manos Katsomallos
2021-09-03 09:28:16 +03:00
parent b4f853f366
commit 70b30443a9
1 changed files with 3 additions and 3 deletions
Download Patch File Download Diff File Expand all files Collapse all files

6
text/related/statistical.tex
View File

@ -363,12 +363,12 @@ The combination of the Perturber and the Grouper follows the sequential composit
% - -
% - 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.
\hypertarget{farokhi2020temporally}{Farokhi}~\cite{farokhi2020temporally} proposed a relaxation of the user-level protection of differential privacy based on the discounted utility theory in economics.
More specifically, at each timestamp, the scheme of \emph{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.
However, the assumption that all users discount previous data releases limits the applicability of the the current scheme in real-world scenarios for statistical data.