Membership Inference Attacks - Edgentiq

Uncovering Hidden Privacy Risks: Entity-Level Membership Inference in Large Language Models

Unmasking Data Privacy Risks: A New Method for Membership Inference Attacks

Unmasking Privacy Risks: Membership Inference in Clinical AI Models

Beyond Bias: New Benchmark Reveals True Limits of Privacy Attacks on Vision-Language AI

Benchmarking Differentially Private Text Generation: Navigating Privacy and Quality in Sensitive Domains

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The Hidden Threat: How Membership Inference Attacks Target Recommender Systems

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Beyond Outputs: Unmasking LLM Training Data Through Internal ‘Neural Breadcrumbs’

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Generative AI: Why GANs May Offer Stronger Privacy Than Diffusion Models

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Balancing Data Privacy and Utility with Curvature-Guided Perturbation

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Unmasking Privacy Vulnerabilities in AI Recommender Systems: New Attacks on LLMs

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Unlearning Data: A Deeper Look at Privacy Risks in AI Models

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Unpacking Privacy Risks in Language Model Knowledge Distillation

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New Attack Method Boosts Privacy Inferences on Small Language Models

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Tracking Privacy Leaks: A Dynamic Look at Deep Learning Models

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Unmasking Privacy Risks in LoRA Fine-tuned Language Models

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Uncovering Hidden Privacy Risks: Entity-Level Membership Inference in Large Language Models

November 4, 2025

Unmasking Data Privacy Risks: A New Method for Membership Inference Attacks

October 28, 2025

Unmasking Privacy Risks: Membership Inference in Clinical AI Models

October 22, 2025