@InProceedings{BeckertKirstenSchefczyk2022,
author = {Bernhard Beckert and
Michael Kirsten and
Michael Schefczyk},
title = {Algorithmic Fairness and Secure Information
Flow (Extended Abstract)},
booktitle = {European Workshop on Algorithmic Fairness ({EWAF} '22),
Lightning round track},
editor = {Christoph Heitz and
Corinna Hertweck and
Eleonora Vigan{\`{o}} and
Michele Loi},
month = jun,
year = {2022},
abstract = {The concept of enforcing secure information flow is well studied in computer science
in the context of information security: If secret information may “flow” through an
algorithm or program in such a way that it can influence the program’s public output,
this is considered insecure information flow, as attackers could potentially observe
(parts of) the secret. There is a wide spectrum of methods and tools to analyse
whether a given program satisfies a given definition of secure information flow.
\newline
We argue that there is a strong correspondence between secure information flow and
algorithmic fairness: if protected attributes such as race, gender, or age are
treated as secret program inputs, then secure information flow means that these
“secret” attributes cannot influence the result of a program.},
url = {https://sites.google.com/view/ewaf22/accepted-papers},
venue = {Z{\"{u}}rich, Switzerland},
eventdate = {2022-06-08/2022-06-09}
}
Algorithmic Fairness and Secure Information Flow (Extended Abstract)
| Autor(en): | Bernhard Beckert, Michael Kirsten und Michael Schefczyk |
|---|---|
| In: | European Workshop on Algorithmic Fairness (EWAF '22), Lightning round track |
| Jahr: | 2022 |
| PDF: | |
| URL: | https://sites.google.com/view/ewaf22/accepted-papers |
Abstract
The concept of enforcing secure information flow is well studied in computer science
in the context of information security: If secret information may “flow” through an
algorithm or program in such a way that it can influence the program’s public output,
this is considered insecure information flow, as attackers could potentially observe
(parts of) the secret. There is a wide spectrum of methods and tools to analyse
whether a given program satisfies a given definition of secure information flow.
We argue that there is a strong correspondence between secure information flow and
algorithmic fairness: if protected attributes such as race, gender, or age are
treated as secret program inputs, then secure information flow means that these
“secret” attributes cannot influence the result of a program.