Inference Attack
86 papers with code • 0 benchmarks • 2 datasets
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MinneApple
Synthetic Keystroke
Latest papers
Membership Inference Attacks on DNNs using Adversarial Perturbations
Secondly, we utilize the framework to propose two novel attacks: (1) Adversarial Membership Inference Attack (AMIA) efficiently utilizes the membership and the non-membership information of the subjects while adversarially minimizing a novel loss function, achieving 6% TPR on both Fashion-MNIST and MNIST datasets; and (2) Enhanced AMIA (E-AMIA) combines EMIA and AMIA to achieve 8% and 4% TPRs on Fashion-MNIST and MNIST datasets respectively, at 1% FPR.
Gaussian Membership Inference Privacy
tleemann/gaussian_mip
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NeurIPS 2023
In particular, we derive a parametric family of $f$-MIP guarantees that we refer to as $\mu$-Gaussian Membership Inference Privacy ($\mu$-GMIP) by theoretically analyzing likelihood ratio-based membership inference attacks on stochastic gradient descent (SGD).
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
kong13661/pia
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Therefore, we also explore the robustness of diffusion models to MIA in the text-to-speech (TTS) task, which is an audio generation task.
Class Attribute Inference Attacks: Inferring Sensitive Class Information by Diffusion-Based Attribute Manipulations
lukasstruppek/class_attribute_inference_attacks
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Neural network-based image classifiers are powerful tools for computer vision tasks, but they inadvertently reveal sensitive attribute information about their classes, raising concerns about their privacy.
Active Membership Inference Attack under Local Differential Privacy in Federated Learning
trucndt/ami
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Federated learning (FL) was originally regarded as a framework for collaborative learning among clients with data privacy protection through a coordinating server.
Towards Unbounded Machine Unlearning
meghdad92/scrub
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NeurIPS 2023
This paper is the first, to our knowledge, to study unlearning for different applications (RB, RC, UP), with the view that each has its own desiderata, definitions for `forgetting' and associated metrics for forget quality.
Membership Inference Attacks against Diffusion Models
fseclab-osaka/mia-diffusion
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We primarily discuss the diffusion model from the standpoints of comparison with a generative adversarial network (GAN) as conventional models and hyperparameters unique to the diffusion model, i. e., time steps, sampling steps, and sampling variances.
Label Inference Attack against Split Learning under Regression Setting
xiehahha/aaai_ppai23_split_learning_leakage
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In this work, we step further to study the leakage in the scenario of the regression model, where the private labels are continuous numbers (instead of discrete labels in classification).
Dissecting Distribution Inference
iamgroot42/dissecting_dist_inf
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A distribution inference attack aims to infer statistical properties of data used to train machine learning models.
Data Origin Inference in Machine Learning
We formally define the data origin and the data origin inference task in the development of the ML model (mainly neural networks).
