Disparate Vulnerability to Membership Inference Attacks
A membership inference attack (MIA) against a machine-learning model enables an attacker to determine whether a given data record was part of the model’s training data or not. In this paper, we provide an in-depth study of the phenomenon of disparate vulnerability against MIAs: unequal success rate...
Saved in:
Main Authors: | Kulynych Bogdan, Yaghini Mohammad, Cherubin Giovanni, Veale Michael, Troncoso Carmela |
---|---|
Format: | article |
Language: | EN |
Published: |
Sciendo
2022
|
Subjects: | |
Online Access: | https://doaj.org/article/c3bf6102ce1b4060b1996827dc5cbec5 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Zen and the art of model adaptation: Low-utility-cost attack mitigations in collaborative machine learning
by: Usynin Dmitrii, et al.
Published: (2022) -
Personal information inference from voice recordings: User awareness and privacy concerns
by: Kröger Jacob Leon, et al.
Published: (2022) -
Privacy-preserving FairSwap: Fairness and privacy interplay
by: Avizheh Sepideh, et al.
Published: (2022) -
From “Onion Not Found” to Guard Discovery
by: Oldenburg Lennart, et al.
Published: (2022) -
Masking Feedforward Neural Networks Against Power Analysis Attacks
by: Athanasiou Konstantinos, et al.
Published: (2022)