Privacy with estimation guarantees
Files
Accepted manuscript
Date
2019
Authors
Wang, Hao
Vo, Lisa
Calmon, Flavio du Pin
Médard, Muriel
Duffy, Ken R.
Varia, Mayank
Version
Accepted manuscript
OA Version
Citation
Hao Wang, Lisa Vo, Flávio du Pin Calmon, Muriel Médard, Ken R Duffy, Mayank Varia. 2019. "Privacy With Estimation Guarantees." IEEE Transactions on Information Theory, Volume 65, Issue 12, pp. 8025 - 8042. https://doi.org./10.1109/TIT.2019.2934414
Abstract
We study the central problem in data privacy: how to share data with an analyst while
providing both privacy and utility guarantees to the user that owns the data. In this setting, we
present an estimation-theoretic analysis of the privacy-utility trade-o (PUT). Here, an analyst
is allowed to reconstruct (in a mean-squared error sense) certain functions of the data (utility),
while other private functions should not be reconstructed with distortion below a certain thresh-
old (privacy). We demonstrate how chi-square information captures the fundamental PUT in
this case and provide bounds for the best PUT. We propose a convex program to compute
privacy-assuring mappings when the functions to be disclosed and hidden are known a priori
and the data distribution is known. We derive lower bounds on the minimum mean-squared
error of estimating a target function from the disclosed data and evaluate the robustness of our
approach when an empirical distribution is used to compute the privacy-assuring mappings in-
stead of the true data distribution. We illustrate the proposed approach through two numerical
experiments.