Inference Attack
107 papers with code • 0 benchmarks • 2 datasets
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MinneApple
Synthetic Keystroke
Most implemented papers
Membership Inference Attacks against Machine Learning Models
We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained.
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.
Membership Inference Attacks From First Principles
privacytrustlab/ml_privacy_meter
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A membership inference attack allows an adversary to query a trained machine learning model to predict whether or not a particular example was contained in the model's training dataset.
Synthesis of Realistic ECG using Generative Adversarial Networks
Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack.
MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples
Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target classifier as an input and predicts the data sample to be a member or non-member of the target classifier's training dataset.
Does CLIP Know My Face?
d0mih/does-clip-know-my-face
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Our large-scale experiments on CLIP demonstrate that individuals used for training can be identified with very high accuracy.
Disparate Vulnerability to Membership Inference Attacks
Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model.
Quantifying identifiability to choose and audit $ε$ in differentially private deep learning
SAP-samples/security-research-identifiability-in-dpdl
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We transform $(\epsilon,\delta)$ to a bound on the Bayesian posterior belief of the adversary assumed by differential privacy concerning the presence of any record in the training dataset.
Membership Inference Attacks on Machine Learning: A Survey
HongshengHu/membership-inference-machine-learning-literature
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In recent years, MIAs have been shown to be effective on various ML models, e. g., classification models and generative models.
Formalizing and Estimating Distribution Inference Risks
iamgroot42/formestdistrisks
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Distribution inference attacks can pose serious risks when models are trained on private data, but are difficult to distinguish from the intrinsic purpose of statistical machine learning -- namely, to produce models that capture statistical properties about a distribution.
