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kategorie
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podkategorie
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pojęcia
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Shadow Model

Artificial intelligence learning model trained by the attacker to mimic the behavior of the target model, used to generate reference data in membership inference attacks.

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Target Model

Machine learning model victim of the membership inference attack, for which the attacker seeks to determine whether it was trained on specific data.

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Privacy Leakage

Leakage of confidential information occurring when a model unintentionally reveals details about its training data through its predictions or behaviors.

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Overfitting Vulnerability

Increased susceptibility to membership inference attacks when the model overfits its training data, producing distinct predictions for seen and unseen examples.

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Confidence Gap Analysis

Attack technique analyzing the gap between the model's confidence scores for training examples versus unseen examples to infer membership.

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Training Data Extraction

Attack more invasive than membership inference, aiming to fully reconstruct training data examples from the model's responses.

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Adversary Knowledge

Level of information available to the attacker about the model architecture, training algorithm, or data distribution, influencing the success of inference attacks.

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Membership Advantage

Metric quantifying an attacker's advantage in membership inference over random guessing, measuring the severity of privacy leakage.

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Regularization Defense

Stratégie de défense utilisant des techniques de régularisation comme le dropout ou la pénalisation L2 pour réduire le surapprentissage et la vulnérabilité aux attaques par inférence.

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Loss Function Modification

Approche défensive modifiant la fonction de perte pendant l'entraînement pour pénaliser les prédictions excessivement confiantes, limitant ainsi les fuites d'information d'appartenance.

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Shadow Dataset

Ensemble de données synthétiques ou réelles utilisé par l'attaquant pour entraîner des modèles ombres, imitant la distribution des données d'entraînement du modèle cible.

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