Membership inference attacks seek to infer the membership of individual training instances of a privately trained model. This paper presents a membership privacy analysis and evaluation system, called MPLens, with three unique contributions. First, through MPLens, we demonstrate how membership inference attack methods …
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48 results for “membership inference”
Paper evaluates membership inference attacks on transfer learning models.
problem Evaluating membership inference attacks on transfer learning models.
method Shadow model training strategy to derive data for membership inference classifier.
result Membership inference attacks can achieve effective performance against transfer learning models.
Proposes a new privacy notion for membership inference attacks on machine learning models.
problem Membership inference attacks on machine learning models.
method Introduces -Membership Inference Privacy (-MIP) and -Gaussian Membership Inference Privacy (-GMIP) to quantify and mitigate privacy risks.
result Analyzes likelihood ratio-based attacks and derives -GMIP guarantees for stochastic gradient descent (SGD) models.
New method bounds membership inference attack success using mutual information.
problem Vulnerability of deep neural networks to membership inference attacks.
method Extended Fano's inequality to measure mutual information between inputs and activations.
result Empirical evaluation shows strong correlation between mutual information and model susceptibility.
New attacks can infer model training membership using only label predictions, not confidence.
problem Inferring whether a data point was used to train a machine learning model.
method Evaluate model's predicted labels under perturbations to infer membership.
result Label-only attacks perform as well as confidence-based attacks and break defenses that rely on confidence masking.
Membership inference determines, given a sample and trained parameters of a machine learning model, whether the sample was part of the training set. In this paper, we derive the optimal strategy for membership inference with a few assumptions on the distribution of the parameters. We show that optimal attacks only depe…
The paper develops methods to infer membership probabilities and rank network nodes using the DCMM model.
problem Understanding the latent structure of network data, especially in mixed-membership models.
method Degree-Corrected Mixed Membership (DCMM) model, novel finite-sample expansion, asymptotic distributions, confidence intervals, multiplier bootstrap method.
result Valid inference on membership probabilities and node rankings, quantifying uncertainty.
Log-Loss scores expose membership privacy breaches.
problem Privacy leakage from statistical aggregates like Log-Loss scores.
method Proved that Log-Loss scores enable full accuracy membership inference in a single query.
result Complete membership privacy breach is possible with Log-Loss scores.
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model's training dataset. To perform members…
New method uses model's generalization gap to predict membership inference attacks.
problem Predicting membership inference attacks on machine learning models.
method Uses the model's generalization gap as a metric to estimate the vulnerability to membership inference attacks.
result Demonstrates that the model's generalization gap provides an upper bound on MIA security.
Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inferencestat.ML
Overparameterized models are more vulnerable to membership inference attacks.
problem Vulnerability of overparameterized models to membership inference attacks.
method Theoretical and empirical analysis of overparameterized linear and ridge-regularized linear regression models in the Gaussian data setting.
result Increased number of parameters and model complexity increase vulnerability to membership inference attacks.
Study shows membership inference requires many more samples than training data.
problem Membership inference attacks require more reference samples than training data.
method Investigated the sample complexity for membership inference in Gaussian mean estimation.
result Ω(n + n^2 ρ^2) samples are necessary for successful membership inference attacks.
New attacks reveal membership in label-only ML models.
problem Vulnerability of ML models to membership inference attacks.
method Developed decision-based membership inference attacks.
result Label-only exposures are vulnerable to membership leakage.
Defending Diffusion Models Against Membership Inference Attacks via Higher-Order Langevin Dynamicscs.LG
Paper defends diffusion models from membership inference attacks using Langevin dynamics.
problem Defending diffusion models against membership inference attacks.
method Uses critically-damped higher-order Langevin dynamics with auxiliary variables.
result Demonstrates improved resistance to membership inference attacks through theoretical investigation and validation.
Large capacity machine learning (ML) models are prone to membership inference attacks (MIAs), which aim to infer whether the target sample is a member of the target model's training dataset. The serious privacy concerns due to the membership inference have motivated multiple defenses against MIAs, e.g., differential pr…
Data privacy is an important issue for "machine learning as a service" providers. We focus on the problem of membership inference attacks: given a data sample and black-box access to a model's API, determine whether the sample existed in the model's training data. Our contribution is an investigation of this problem in…
Generative text classifiers are most vulnerable to membership inference attacks.
problem Privacy threat from Membership Inference Attacks on generative text classifiers.
method Comprehensive empirical evaluation of generative, discriminative, and pseudo-generative classifiers across various datasets.
result Generative classifiers explicitly modeling are most vulnerable to membership leakage.
Study quantized models' privacy against membership inference attacks.
problem Privacy risk in quantized machine learning models.
method Proposed a new MIS indicator for post-training quantization procedures, minimizing empirical loss.
result Demonstrated effectiveness of new MIS indicator in assessing and ranking privacy risk.
The paper proposes a new model to analyze directed networks and accurately estimate community memberships.
problem Modeling and estimating community memberships in directed networks with heterogeneous degrees.
method Directed Degree Corrected Mixed Membership (DiDCMM) model and DiMSC algorithm.
result The proposed DiMSC algorithm is asymptotically consistent and provides error bounds for community membership vectors.
Study membership inference under skewed priors and adaptive thresholds, improving attack accuracy.
problem Membership inference in imbalanced settings with selective thresholding.
method Developed PPV metric for skewed priors, threshold selection procedure, and a new inference attack.
result Improved inference attack accuracy in imbalanced settings.
New model quantifies how much machine learning models can reveal about individual data usage.
problem Measuring and reducing the leakage of membership information from machine learning models.
method Using information theory, conditional mutual information leakage, and Kullback-Leibler divergence to quantify and bound the leakage.
result The amount of membership information leakage is reduced by adding Gaussian -differentially-private additive noises.
Machine learning models leak information about the datasets on which they are trained. An adversary can build an algorithm to trace the individual members of a model's training dataset. As a fundamental inference attack, he aims to distinguish between data points that were part of the model's training set and any other…
Membership inference (MI) attacks exploit the fact that machine learning algorithms sometimes leak information about their training data through the learned model. In this work, we study membership inference in the white-box setting in order to exploit the internals of a model, which have not been effectively utilized …
New method detects if data points were used in training models with low cost and high power.
problem Detecting if a particular data point was used in training a model.
method Fine-grained modeling of null hypothesis in likelihood ratio tests, leveraging reference models and population data.
result RMIA has superior test power compared to prior methods, even at extremely low false positive rates.
New attack reveals memorization patterns in pre-trained LLMs.
problem Determining if a data point was part of a pre-trained LLM's training set.
method Adapts MIA statistical tests to LLM's perplexity dynamics of subsequences.
result Significantly outperforms prior approaches in membership inference attacks.
We consider the problem of estimating community memberships of nodes in a network, where every node is associated with a vector determining its degree of membership in each community. Existing provably consistent algorithms often require strong assumptions about the population, are computationally expensive, and only p…
Regularization can improve both privacy and performance in machine learning models.
problem Privacy vs. Utility trade-off in machine learning models.
method The study uses logistic regression with ridge regularization and a leave-one-out analysis tool.
result Increasing the number of parameters can improve both privacy and performance when coupled with proper regularization.
MI attacks often mislabel non-training samples, making them impractical.
problem MI attacks mislabel non-training samples, leading to high false positive rate.
method Analyzed new features like distance to decision boundary and gradient norms.
result MI attacks cannot achieve high accuracy and low false positive rate simultaneously.
This paper analyzes how data and model properties affect membership inference attacks.
problem Understanding and mitigating the vulnerability of machine learning models to membership inference attacks.
method Empirical analysis of data and model properties on MIA success.
result Data and model properties, not just model overfitting, influence MIA success.
A new, low-cost method speeds up membership inference attacks on large language models.
problem Membership inference attacks on large language models.
method An ensemble of small quantile regression models to determine model training set membership.
result Comparable or improved accuracy with significantly reduced computational cost.
SIMPLE-RC method tests group membership profiles in large networks with weak signals.
problem Testing group membership profiles in large networks with weak signals.
method Random coupling technique to construct maximum SIMPLE tests for subsampled node pairs.
result Asymptotic distributions of SIMPLE-RC test are derived, enabling delicate analysis.
Proposes MIP, a privacy notion that requires less randomness than DP, leading to better utility.
problem Preserving privacy in machine learning models with sensitive data.
method Introduces membership inference privacy (MIP) as a new privacy notion and shows its relationship with differential privacy (DP).
result MIP can be achieved with less randomness than DP, resulting in better utility.
In this paper we propose a new membership attack method called co-membership attacks against deep generative models including Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). Specifically, membership attack aims to check whether a given instance x was used in the training data or not. A co-me…
Observations consisting of measurements on relationships for pairs of objects arise in many settings, such as protein interaction and gene regulatory networks, collections of author-recipient email, and social networks. Analyzing such data with probabilisic models can be delicate because the simple exchangeability assu…
Machine unlearning can compromise privacy, study shows.
problem Machine unlearning may leave data imprints in ML models, risking privacy.
method Proposed a membership inference attack to detect leakage.
result Machine unlearning can lead to unintended privacy risks.
This paper analyzes privacy risks in neural network pruning and proposes a defense mechanism.
problem Privacy risks in neural network pruning due to membership inference attacks.
method Investigates the impact of pruning on prediction divergence and proposes a self-attention membership inference attack.
result Proposed defense mechanism mitigates privacy risks while maintaining sparsity and accuracy.
Paper develops a method to detect training data used in machine learning models.
problem Detecting training data used in machine learning models.
method Measures leakage of training data using a proxy of a model's total variation near training samples.
result Empirical evidence supports the effectiveness of the proposed method.
New bounds on machine learning data leakage identified.
problem Machine Learning models can leak sensitive information.
method Formalized attack setups, derived universal bounds, studied mutual information.
result Connected attack success rate to generalization gap and mutual information.
The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular framework for modeling social network relationships. It can fully exploit each individual node's participation (or membership) in a social structure. Despite its powerful representations, this model makes an assumption that the distributions of relat…
Pruning improves DNNs against MIA while reducing model size and computation.
problem Vulnerability of DNNs to membership inference attacks (MIA).
method Proposes a pruning algorithm to reduce model size and computational operations.
result Pruned subnetwork prevents privacy leakage from MIA with competitive accuracy.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
problem Modeling directed mixed membership networks with distinct community structures.
method Directed Mixed Membership Stochastic Blockmodel (DiMMSB) with DiSP algorithm.
result DiSP algorithm is asymptotically consistent and outperforms competitors.
Regularization may not fully protect against membership inference attacks.
problem Mitigating membership inference attacks in deep learning models.
method Evaluation of 8 regularization mechanisms on three image classification tasks.
result Certain regularization mechanisms can inadvertently help membership inference attacks.
The arms race between attacks and defenses for machine learning models has come to a forefront in recent years, in both the security community and the privacy community. However, one big limitation of previous research is that the security domain and the privacy domain have typically been considered separately. It is t…
Paper tackles MIAs vulnerability by controlling FDR, providing guarantees on false discoveries.
problem Vulnerability of deep learning models to membership inference attacks (MIAs).
method Designs a novel membership inference attack method that provides FDR guarantees.
result Demonstrates the effectiveness of the method in various settings.
Study compares federated learning and coreset approaches for privacy in distributed machine learning.
problem Measuring privacy in distributed machine learning approaches.
method Comparison of federated learning and coreset approaches using membership inference attack.
result Federated learning offers better privacy than coreset, but with higher communication cost.
TAMIS improves MIA on synthetic data, reducing cost and requiring less knowledge.
problem Empirical assessment of privacy in machine learning algorithms.
method Improves MAMA-MIA by recovering graphical model from synthetic data and introducing a more accurate attack score.
result TAMIS achieves better or similar performance to MAMA-MIA on synthetic data challenges.
Enhanced attacks quantify machine learning data leakage.
problem Quantifying how much machine learning models reveal about their training data.
method Hypothesis testing framework for membership inference attacks.
result New attacks achieve higher true positive rates with lower false positive rates.
We propose a stochastic gradient Markov chain Monte Carlo (SG-MCMC) algorithm for scalable inference in mixed-membership stochastic blockmodels (MMSB). Our algorithm is based on the stochastic gradient Riemannian Langevin sampler and achieves both faster speed and higher accuracy at every iteration than the current sta…