Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods

• URL: http://arxiv.org/abs/2411.05743v2
• Date: Mon, 10 Feb 2025 12:04:29 GMT
• Title: Free Record-Level Privacy Risk Evaluation Through Artifact-Based Methods
• Authors: Joseph Pollock, Igor Shilov, Euodia Dodd, Yves-Alexandre de Montjoye,
• Abstract summary: Membership inference attacks (MIAs) are widely used to assess privacy risks in machine learning models.<n>State-of-the-art methods require training hundreds of shadow models with the same architecture as the target model.<n>We propose a novel approach for identifying the training samples most vulnerable to membership inference attacks by analyzing artifacts naturally available during the training process.
• Score: 6.902279764206365
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Membership inference attacks (MIAs) are widely used to empirically assess privacy risks in machine learning models, both providing model-level vulnerability metrics and identifying the most vulnerable training samples. State-of-the-art methods, however, require training hundreds of shadow models with the same architecture as the target model. This makes the computational cost of assessing the privacy of models prohibitive for many practical applications, particularly when used iteratively as part of the model development process and for large models. We propose a novel approach for identifying the training samples most vulnerable to membership inference attacks by analyzing artifacts naturally available during the training process. Our method, Loss Trace Interquantile Range (LT-IQR), analyzes per-sample loss trajectories collected during model training to identify high-risk samples without requiring any additional model training. Through experiments on standard benchmarks, we demonstrate that LT-IQR achieves 92% precision@k=1% in identifying the samples most vulnerable to state-of-the-art MIAs. This result holds across datasets and model architectures with LT-IQR outperforming both traditional vulnerability metrics, such as loss, and lightweight MIAs using few shadow models. We also show LT-IQR to accurately identify points vulnerable to multiple MIA methods and perform ablation studies. We believe LT-IQR enables model developers to identify vulnerable training samples, for free, as part of the model development process. Our results emphasize the potential of artifact-based methods to efficiently evaluate privacy risks.

Related papers

• A hierarchical approach for assessing the vulnerability of tree-based classification models to membership inference attack [0.552480439325792]
  Machine learning models can inadvertently expose confidential properties of their training data, making them vulnerable to membership inference attacks (MIA) This article presents two new complementary approaches for efficiently identifying vulnerable tree-based models.
  arXiv  Detail & Related papers  (2025-02-13T15:16:53Z)
• EM-MIAs: Enhancing Membership Inference Attacks in Large Language Models through Ensemble Modeling [2.494935495983421]
  This paper proposes a novel ensemble attack method that integrates several existing MIAs techniques into an XGBoost-based model to enhance overall attack performance (EM-MIAs) Experimental results demonstrate that the ensemble model significantly improves both AUC-ROC and accuracy compared to individual attack methods across various large language models and datasets.
  arXiv  Detail & Related papers  (2024-12-23T03:47:54Z)
• Forewarned is Forearmed: Leveraging LLMs for Data Synthesis through Failure-Inducing Exploration [90.41908331897639]
  Large language models (LLMs) have significantly benefited from training on diverse, high-quality task-specific data. We present a novel approach, ReverseGen, designed to automatically generate effective training samples.
  arXiv  Detail & Related papers  (2024-10-22T06:43:28Z)
• Order of Magnitude Speedups for LLM Membership Inference [5.124111136127848]
  Large Language Models (LLMs) have the promise to revolutionize computing broadly, but their complexity and extensive training data also expose privacy vulnerabilities. One of the simplest privacy risks associated with LLMs is their susceptibility to membership inference attacks (MIAs) We propose a low-cost MIA that leverages an ensemble of small quantile regression models to determine if a document belongs to the model's training set or not.
  arXiv  Detail & Related papers  (2024-09-22T16:18:14Z)
• Learning Augmentation Policies from A Model Zoo for Time Series Forecasting [58.66211334969299]
  We introduce AutoTSAug, a learnable data augmentation method based on reinforcement learning. By augmenting the marginal samples with a learnable policy, AutoTSAug substantially improves forecasting performance.
  arXiv  Detail & Related papers  (2024-09-10T07:34:19Z)
• Querying Easily Flip-flopped Samples for Deep Active Learning [63.62397322172216]
  Active learning is a machine learning paradigm that aims to improve the performance of a model by strategically selecting and querying unlabeled data. One effective selection strategy is to base it on the model's predictive uncertainty, which can be interpreted as a measure of how informative a sample is. This paper proposes the it least disagree metric (LDM) as the smallest probability of disagreement of the predicted label.
  arXiv  Detail & Related papers  (2024-01-18T08:12:23Z)
• Low-Cost High-Power Membership Inference Attacks [15.240271537329534]
  Membership inference attacks aim to detect if a particular data point was used in training a model. We design a novel statistical test to perform robust membership inference attacks with low computational overhead. RMIA lays the groundwork for practical yet accurate data privacy risk assessment in machine learning.
  arXiv  Detail & Related papers  (2023-12-06T03:18:49Z)
• QualEval: Qualitative Evaluation for Model Improvement [82.73561470966658]
  We propose QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement. QualEval uses a powerful LLM reasoner and our novel flexible linear programming solver to generate human-readable insights. We demonstrate that leveraging its insights, for example, improves the absolute performance of the Llama 2 model by up to 15% points relative.
  arXiv  Detail & Related papers  (2023-11-06T00:21:44Z)
• SCME: A Self-Contrastive Method for Data-free and Query-Limited Model Extraction Attack [18.998300969035885]
  Model extraction attacks fool the target model by generating adversarial examples on a substitute model. We propose a novel data-free model extraction method named SCME, which considers both the inter- and intra-class diversity in synthesizing fake data.
  arXiv  Detail & Related papers  (2023-10-15T10:41:45Z)
• Beyond Labeling Oracles: What does it mean to steal ML models? [52.63413852460003]
  Model extraction attacks are designed to steal trained models with only query access. We investigate factors influencing the success of model extraction attacks. Our findings urge the community to redefine the adversarial goals of ME attacks.
  arXiv  Detail & Related papers  (2023-10-03T11:10:21Z)
• Unstoppable Attack: Label-Only Model Inversion via Conditional Diffusion Model [14.834360664780709]
  Model attacks (MIAs) aim to recover private data from inaccessible training sets of deep learning models. This paper develops a novel MIA method, leveraging a conditional diffusion model (CDM) to recover representative samples under the target label. Experimental results show that this method can generate similar and accurate samples to the target label, outperforming generators of previous approaches.
  arXiv  Detail & Related papers  (2023-07-17T12:14:24Z)
• Unleashing Mask: Explore the Intrinsic Out-of-Distribution Detection Capability [70.72426887518517]
  Out-of-distribution (OOD) detection is an indispensable aspect of secure AI when deploying machine learning models in real-world applications. We propose a novel method, Unleashing Mask, which aims to restore the OOD discriminative capabilities of the well-trained model with ID data. Our method utilizes a mask to figure out the memorized atypical samples, and then finetune the model or prune it with the introduced mask to forget them.
  arXiv  Detail & Related papers  (2023-06-06T14:23:34Z)
• Self-Destructing Models: Increasing the Costs of Harmful Dual Uses of Foundation Models [103.71308117592963]
  We present an algorithm for training self-destructing models leveraging techniques from meta-learning and adversarial learning. In a small-scale experiment, we show MLAC can largely prevent a BERT-style model from being re-purposed to perform gender identification.
  arXiv  Detail & Related papers  (2022-11-27T21:43:45Z)
• Membership Inference Attacks by Exploiting Loss Trajectory [19.900473800648243]
  We propose a new attack method, called system, which can exploit the membership information from the whole training process of the target model. Our attack achieves at least 6$times$ higher true-positive rate at a low false-positive rate of 0.1% than existing methods.
  arXiv  Detail & Related papers  (2022-08-31T16:02:26Z)
• Leveraging Adversarial Examples to Quantify Membership Information Leakage [30.55736840515317]
  We develop a novel approach to address the problem of membership inference in pattern recognition models. We argue that this quantity reflects the likelihood of belonging to the training data. Our method performs comparable or even outperforms state-of-the-art strategies.
  arXiv  Detail & Related papers  (2022-03-17T19:09:38Z)
• Leveraging Unlabeled Data to Predict Out-of-Distribution Performance [63.740181251997306]
  Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions. In this work, we investigate methods for predicting the target domain accuracy using only labeled source data and unlabeled target data. We propose Average Thresholded Confidence (ATC), a practical method that learns a threshold on the model's confidence, predicting accuracy as the fraction of unlabeled examples.
  arXiv  Detail & Related papers  (2022-01-11T23:01:12Z)
• Sample-Efficient Reinforcement Learning via Conservative Model-Based Actor-Critic [67.00475077281212]
  Model-based reinforcement learning algorithms are more sample efficient than their model-free counterparts. We propose a novel approach that achieves high sample efficiency without the strong reliance on accurate learned models. We show that CMBAC significantly outperforms state-of-the-art approaches in terms of sample efficiency on several challenging tasks.
  arXiv  Detail & Related papers  (2021-12-16T15:33:11Z)
• Reconstructing Training Data from Diverse ML Models by Ensemble Inversion [8.414622657659168]
  Model Inversion (MI), in which an adversary abuses access to a trained Machine Learning (ML) model, has attracted increasing research attention. We propose an ensemble inversion technique that estimates the distribution of original training data by training a generator constrained by an ensemble of trained models. We achieve high quality results without any dataset and show how utilizing an auxiliary dataset that's similar to the presumed training data improves the results.
  arXiv  Detail & Related papers  (2021-11-05T18:59:01Z)
• ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models [64.03398193325572]
  Inference attacks against Machine Learning (ML) models allow adversaries to learn about training data, model parameters, etc. We concentrate on four attacks - namely, membership inference, model inversion, attribute inference, and model stealing. Our analysis relies on a modular re-usable software, ML-Doctor, which enables ML model owners to assess the risks of deploying their models.
  arXiv  Detail & Related papers  (2021-02-04T11:35:13Z)
• How Robust are Randomized Smoothing based Defenses to Data Poisoning? [66.80663779176979]
  We present a previously unrecognized threat to robust machine learning models that highlights the importance of training-data quality. We propose a novel bilevel optimization-based data poisoning attack that degrades the robustness guarantees of certifiably robust classifiers. Our attack is effective even when the victim trains the models from scratch using state-of-the-art robust training methods.
  arXiv  Detail & Related papers  (2020-12-02T15:30:21Z)
• SAMBA: Safe Model-Based & Active Reinforcement Learning [59.01424351231993]
  SAMBA is a framework for safe reinforcement learning that combines aspects from probabilistic modelling, information theory, and statistics. We evaluate our algorithm on a variety of safe dynamical system benchmarks involving both low and high-dimensional state representations. We provide intuition as to the effectiveness of the framework by a detailed analysis of our active metrics and safety constraints.
  arXiv  Detail & Related papers  (2020-06-12T10:40:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.