Introduces noise aggregation analysis with single-step small-noise injection to enable efficient and accurate membership inference attacks on diffusion models.
Gan-leaks: A taxonomy of membership inference attacks against generative models,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Generative models for trajectory data do not inherently preserve privacy, as membership inference attacks can identify training data points in representative models.
citing papers explorer
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Noise Aggregation Analysis Driven by Small-Noise Injection: Efficient Membership Inference for Diffusion Models
Introduces noise aggregation analysis with single-step small-noise injection to enable efficient and accurate membership inference attacks on diffusion models.
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Privacy Evaluation of Generative Models for Trajectory Generation
Generative models for trajectory data do not inherently preserve privacy, as membership inference attacks can identify training data points in representative models.