Wang, Hao and Vo, Lisa and Calmon, Flavio P. and Medard, Muriel and Duffy, Ken R. and Varia, Mayank
(2019)
Privacy with Estimation Guarantees.
IEEE Transactions on Information Theory, 65 (12).
pp. 8025-8042.
ISSN 0018-9448
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-off (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 threshold (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 instead of the true data distribution.
We illustrate the proposed approach through two numerical
experiments.
Item Type: |
Article
|
Additional Information: |
This is the preprint version of the published article, which is available at H. Wang, L. Vo, F. P. Calmon, M. Médard, K. R. Duffy and M. Varia, "Privacy With Estimation Guarantees," in IEEE Transactions on Information Theory, vol. 65, no. 12, pp. 8025-8042, Dec. 2019, doi: 10.1109/TIT.2019.2934414. |
Keywords: |
Estimation; privacy-utility trade-off; minimum
mean-squared error; |
Academic Unit: |
Faculty of Science and Engineering > Research Institutes > Hamilton Institute |
Item ID: |
13479 |
Identification Number: |
https://doi.org/10.1109/TIT.2019.2934414 |
Depositing User: |
Dr Ken Duffy
|
Date Deposited: |
03 Nov 2020 15:29 |
Journal or Publication Title: |
IEEE Transactions on Information Theory |
Publisher: |
IEEE |
Refereed: |
Yes |
URI: |
|
Use Licence: |
This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available
here |
Repository Staff Only(login required)
|
Item control page |
Downloads per month over past year
Origin of downloads