Sökning: onr:"swepub:oai:DiVA.org:kth-312046" >
A Variational Appro...
A Variational Approach to Privacy and Fairness
-
- Rodríguez Gálvez, Borja (författare)
- KTH,Teknisk informationsvetenskap
-
- Thobaben, Ragnar (författare)
- KTH,Teknisk informationsvetenskap
-
- Skoglund, Mikael, 1969- (författare)
- KTH,Teknisk informationsvetenskap
-
(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2021
- 2021
- Engelska.
-
Ingår i: 2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE).
- Relaterad länk:
-
https://urn.kb.se/re...
-
visa fler...
-
https://doi.org/10.1...
-
visa färre...
Abstract
Ämnesord
Stäng
- In this article, we propose a new variational approach to learn private and/or fair representations. This approach is based on the Lagrangians of a new formulation of the privacy and fairness optimization problems that we propose. In this formulation, we aim to generate representations of the data that keep a prescribed level of the relevant information that is not shared by the private or sensitive data, while minimizing the remaining information they keep. The proposed approach (i) exhibits the similarities of the privacy and fairness problems, (ii) allows us to control the trade-off between utility and privacy or fairness through the Lagrange multiplier parameter, and (iii) can be comfortably incorporated to common representation learning algorithms such as the VAE, the β-VAE, the VIB, or the nonlinear IB.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Nyckelord
- Economic and social effects
- Fair representation
- Fairness problem
- Learn
- Optimization problems
- Privacy problems
- Private data
- Sensitive datas
- Trade off
- Variational approaches
- Lagrange multipliers
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)