@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 |
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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.