Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity

Membership Inference Attacks Fueled by Few-Shot Learning to Detect Privacy Leakage and Address Data Integrity

Deep learning models have an intrinsic privacy issue as they memorize parts of their training data, creating a privacy leakage. Membership inference attacks (MIAs) exploit this to obtain confidential information about the data used for training, aiming to steal information. They can be repurposed as...

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Bibliographic Details
Main Authors: Daniel Jiménez-López, Nuria Rodríguez-Barroso, M. Victoria Luzón, Javier Del Ser, Francisco Herrera
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Machine Learning and Knowledge Extraction
Subjects:
deep learning
membership inference attacks
data integrity
privacy evaluation
few-shot learning
Online Access:https://www.mdpi.com/2504-4990/7/2/43
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