Membership Inference Attacks

LeakPro supports a wide range of attack scenarios targeting different privacy vulnerabilities in machine learning models. This section provides an in-depth look at each scenario, including supported attack types, data modalities, and references to specific implementations.

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3.1 Membership Inference Attacks

Attack Types

• Label-Only Attacks: Infer membership using only predicted labels.
• Logit-Based Attacks: Use model confidence scores (logits) to increase inference accuracy.

Supported Data Modalities

• Image Data: Vision models trained on datasets like CIFAR-10 and MNIST.
• Text Data: NLP models such as sentiment analyzers.
• Tabular Data: Structured datasets like financial or healthcare records.
• Graph Data: Graph neural networks used in social network analysis.

Implemented Attacks Reference

Implemented Attacks References

Implemented Attacks References

This table lists all implemented attacks in LeakPro across different scenarios, linking directly to the code implementation and original research papers.

Attack	Adversary Access	Code Implementation	Original Paper
HSJ	Label-Based	LeakPro Code	Choquette-Choo et al., in ICML (2021)
LiRA	Logit-Based	LeakPro Code	Carlini et al., IEEE SP (2022)
TrajectoryMIA	Logit-Based	LeakPro Code	Liu et al., in ACM SIGSAC (2022)
P-Attack	Loss-Based	LeakPro Code	Ye et al., in ACM CCS (2022)
QMIA	Logit-Based	LeakPro Code	Bertran et al., in NeurIPS (2023)
RMIA	Logit-Based	LeakPro Code	Zarifzadeh et al., in ICML (2024)
YOQO	Label-Based	LeakPro Code	Wu et al., in ICLR (2024)

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How to Use This Table

• Click on the Code Implementation links to explore the corresponding LeakPro modules.
• Click on the Original Paper links to read the foundational research behind each attack.