Hugging Face Trending Papers
Power Side-Channel Membership Inference Attack on Embedded Machine Learning
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arXiv Machine Learning
Oct 9
Power Side-Channel Membership Inference Attack on Embedded Machine Learning
The paper introduces PSCMIA, a power side‑channel membership inference attack that can determine whether a data sample was used to train an embedded machine learning model by analyzing power traces, without needing prediction probabilities or labels. The authors evaluate PSCMIA on MNIST, FMNIST, CIFAR10, and CINIC10 datasets using fully connected and convolutional neural networks on STM32F3 and XMEGA platforms, achieving ROC‑AUC values up to 0.907 for fully connected models and close performance to probability‑vector attacks for CNNs. PSCMIA outperforms label‑only attacks in 11 of 16 configurations, showing that physical execution can leak membership information even when conventional outputs are suppressed.
arXiv Machine Learning
Oct 8
Estimating Model-Level Membership Inference Vulnerability Without Reference Models
The paper introduces a method to estimate a model’s vulnerability to the Likelihood Ratio Attack (LiRA) without training reference models, using only the target model’s train and test loss distributions. It shows that LiRA’s per‑sample signal can be decomposed into a variance‑ratio term and a residual mean‑shift term, and that different loss‑distribution shapes dictate which reference‑free proxy to use. Two proxies are presented: the LOSS attack TNR for heavy‑tailed losses and the LOSS attack AUC for symmetric losses, both achieving low RMSE in predicting LiRA TPR across multiple architectures and datasets.
arXiv Machine Learning
Jun 24
LoMime: Query-Efficient Membership Inference using Model Extraction in Label-Only Settings
arXiv:2602. 18934v2 Announce Type: replace Abstract: Membership inference attacks (MIAs) threaten the privacy of machine learning models by revealing whether a specific data point was used during training.
arXiv Machine Learning
Sep 14
Membership Inference via Pairwise Likelihood Ratios
The paper introduces Pairwise Likelihood MIA (PL‑MIA), a unified membership inference attack that combines a Gaussian likelihood‑ratio statistic with population calibration and the Cauchy combination test. PL‑MIA generates p‑values from pairwise comparisons between a query point and reference points, then aggregates these continuous signals using the Cauchy test to preserve evidence strength. Experiments show that PL‑MIA surpasses strong baselines, boosting true positive rates by over 25% in low‑false‑positive settings, thereby validating the theoretical advantages of the proposed statistical framework.
arXiv Machine Learning
Sep 18
Automated Membership Inference Attacks (AutoMIA): Discovering MIA Signal Computations using LLM Agents
The paper introduces AutoMIA, a framework that uses large language model agents to automatically design and implement new membership inference attack (MIA) signal computations. By systematically exploring a wide range of attack strategies, AutoMIA discovers novel MIAs tailored to specific target models and datasets, achieving up to a 0.18 absolute improvement in AUC over existing methods. This demonstrates that LLM agents can serve as an effective and scalable approach for creating state‑of‑the‑art MIAs.
arXiv Machine Learning
Jul 16
When T2I Synthetic Data Backfires: Amplified Privacy Risks in Real-Synthetic Mix Training
arXiv:2607. 13541v1 Announce Type: cross Abstract: To overcome data scarcity and privacy constraints in data collection, it has become standard practice across academia and industry to augment real training data with text-to-image (T2I)-generated synthetic data, a paradigm we term Real-Synthetic Mix-Training (RSMT).