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Defenses to Membership Inference Attacks: A Survey

https://doi.org/10.1145/3620667

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Enhanced Mixup Training: a Defense Method Against Membership Inference Attack

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Protect privacy of deep classification networks by exploiting their generative power

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Privacy Risks from Genomic Data-Sharing Beacons

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Digestive neural networks: A novel defense strategy against inference attacks in federated learning

inferredcites →https://doi.org/10.1016/j.neucom.2021.04.082openalex-snapshot

Resisting membership inference attacks through knowledge distillation

inferredcites →https://doi.org/10.1093/comjnl/bxac080openalex-snapshot

Evaluating Membership Inference Through Adversarial Robustness

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Differential Privacy Preservation in Deep Learning: Challenges, Opportunities and Solutions

inferredcites →https://doi.org/10.1109/csf.2018.00027openalex-snapshot

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

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Efficient object localization using Convolutional Networks

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Rethinking the Inception Architecture for Computer Vision

inferredcites →https://doi.org/10.1109/cvpr52688.2022.01015openalex-snapshot

Leveraging Adversarial Examples to Quantify Membership Information Leakage

inferredcites →https://doi.org/10.1109/iccv48922.2021.01455openalex-snapshot

Membership Inference Attacks are Easier on Difficult Problems

inferredcites →https://doi.org/10.1109/icpr48806.2021.9413067openalex-snapshot

Generating Private Data Surrogates for Vision Related Tasks

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EAR: An Enhanced Adversarial Regularization Approach against Membership Inference Attacks

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A Secure Federated Learning Framework for 5G Networks

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Membership Inference Attacks Against Machine Learning Models

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Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference Attacks against Centralized and Federated Learning

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Adversary Instantiation: Lower Bounds for Differentially Private Machine Learning

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Membership Inference Attacks From First Principles

inferredcites →https://doi.org/10.1109/tdsc.2021.3088480openalex-snapshot

Leveraging Domain Adaptation as a Defense Against Membership Inference Attacks

inferredcites →https://doi.org/10.1109/tdsc.2022.3174569openalex-snapshot

Defending Against Membership Inference Attacks With High Utility by GAN

inferredcites →https://doi.org/10.1109/tmc.2022.3148690openalex-snapshot

Membership Inference Attack and Defense for Wireless Signal Classifiers With Deep Learning

inferredcites →https://doi.org/10.1109/tps-isa48467.2019.00019openalex-snapshot

Effects of Differential Privacy and Data Skewness on Membership Inference Vulnerability

inferredcites →https://doi.org/10.1109/tsc.2019.2897554openalex-snapshot

Demystifying Membership Inference Attacks in Machine Learning as a Service

inferredcites →https://doi.org/10.1109/tsusc.2019.2930526openalex-snapshot

MIASec: Enabling Data Indistinguishability Against Membership Inference Attacks in MLaaS

inferredcites →https://doi.org/10.1109/wacv57701.2024.00482openalex-snapshot

Membership Inference Attack Using Self Influence Functions

inferredcites →https://doi.org/10.1142/9789811232701_0003openalex-snapshot

Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data

inferredcites →https://doi.org/10.1145/2976749.2978318openalex-snapshot

Deep Learning with Differential Privacy

inferredcites →https://doi.org/10.1145/3243734.3243855openalex-snapshot

Machine Learning with Membership Privacy using Adversarial Regularization

inferredcites →https://doi.org/10.1145/3319535.3363201openalex-snapshot

MemGuard

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LDP-Fed

inferredcites →https://doi.org/10.1145/3411501.3419428openalex-snapshot

Privacy-Preserving in Defending against Membership Inference Attacks

inferredcites →https://doi.org/10.1145/3422337.3447836openalex-snapshot

Membership Inference Attacks and Defenses in Classification Models

inferredcites →https://doi.org/10.1145/3447548.3467444openalex-snapshot

Defending Privacy Against More Knowledgeable Membership Inference Attackers

inferredcites →https://doi.org/10.1145/3447548.3467445openalex-snapshot

PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks

inferredcites →https://doi.org/10.1145/3460120.3484770openalex-snapshot

Membership Inference Attacks Against Recommender Systems

inferredcites →https://doi.org/10.1145/3474369.3486876openalex-snapshot

Differential Privacy Defenses and Sampling Attacks for Membership Inference

inferredcites →https://doi.org/10.1145/3488932.3497772openalex-snapshot

Membership Feature Disentanglement Network

inferredcites →https://doi.org/10.1145/3488932.3501279openalex-snapshot

Understanding Disparate Effects of Membership Inference Attacks and their Countermeasures

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Membership Inference Attacks on Machine Learning: A Survey

inferredcites →https://doi.org/10.1145/3548606.3560675openalex-snapshot

Enhanced Membership Inference Attacks against Machine Learning Models

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Membership Inference Attacks by Exploiting Loss Trajectory

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Repeated Knowledge Distillation with Confidence Masking to Mitigate Membership Inference Attacks

inferredcites →https://doi.org/10.1145/3564625.3567986openalex-snapshot

NeuGuard: Lightweight Neuron-Guided Defense against Membership Inference Attacks

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Defending Against Membership Inference Attacks on Beacon Services

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A Survey of Privacy Attacks in Machine Learning

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Controlling the signal: Practical privacy protection of genomic data sharing through Beacon services

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A survey of transfer learning

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Research Progress and Challenges of Membership Inference Attacks in Machine Learning

inferredcites →https://doi.org/10.14722/ndss.2019.23119openalex-snapshot

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

inferredcites →https://doi.org/10.14722/ndss.2021.24293openalex-snapshot

Practical Blind Membership Inference Attack via Differential Comparisons

inferredcites →https://doi.org/10.1609/aaai.v33i01.33011190openalex-snapshot

Private Model Compression via Knowledge Distillation

inferredcites →https://doi.org/10.1609/aaai.v34i04.6107openalex-snapshot

Characterizing Membership Privacy in Stochastic Gradient Langevin Dynamics

inferredcites →https://doi.org/10.1609/aaai.v35i11.17150openalex-snapshot

Membership Privacy for Machine Learning Models Through Knowledge Transfer

inferredcites →https://doi.org/10.1609/aaai.v37i9.26289openalex-snapshot

Purifier: Defending Data Inference Attacks via Transforming Confidence Scores

inferredcites →https://doi.org/10.2478/popets-2022-0050openalex-snapshot

Knowledge Cross-Distillation for Membership Privacy

inferredcites →https://doi.org/10.24963/ijcai.2021/432openalex-snapshot

Against Membership Inference Attack: Pruning is All You Need

inferredcites →https://doi.org/10.25394/pgs.24983289openalex-snapshot

MEMBERSHIP INFERENCE ATTACKS AND DEFENSES IN CLASSIFICATION MODELS

inferredcites →https://doi.org/10.4236/jcc.2021.95007openalex-snapshot

Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise

inferredcites →https://doi.org/10.48550/arxiv.1207.0580openalex-snapshot

Improving neural networks by preventing co-adaptation of feature detectors

inferredcites →https://doi.org/10.48550/arxiv.1503.02531openalex-snapshot

Distilling the Knowledge in a Neural Network

inferredcites →https://doi.org/10.48550/arxiv.1610.05755openalex-snapshot

Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data

inferredcites →https://doi.org/10.48550/arxiv.1802.04889openalex-snapshot

Understanding Membership Inferences on Well-Generalized Learning Models

inferredcites →https://doi.org/10.48550/arxiv.1802.06739openalex-snapshot

Differentially Private Generative Adversarial Network

inferredcites →https://doi.org/10.48550/arxiv.1803.03148openalex-snapshot

Generating Artificial Data for Private Deep Learning

inferredcites →https://doi.org/10.48550/arxiv.1807.01860openalex-snapshot

Privacy-preserving Machine Learning through Data Obfuscation

inferredcites →https://doi.org/10.48550/arxiv.1908.07882openalex-snapshot

Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection

inferredcites →https://doi.org/10.48550/arxiv.2001.00071openalex-snapshot

privGAN: Protecting GANs from membership inference attacks at low cost

inferredcites →https://doi.org/10.48550/arxiv.2001.08855openalex-snapshot

Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

inferredcites →https://doi.org/10.48550/arxiv.2002.06856openalex-snapshot

Data and Model Dependencies of Membership Inference Attack

inferredcites →https://doi.org/10.48550/arxiv.2005.03915openalex-snapshot

Defending Model Inversion and Membership Inference Attacks via Prediction Purification

inferredcites →https://doi.org/10.48550/arxiv.2006.05336openalex-snapshot

On the Effectiveness of Regularization Against Membership Inference Attacks

inferredcites →https://doi.org/10.48550/arxiv.2011.13696openalex-snapshot

Use the Spear as a Shield: A Novel Adversarial Example based Privacy-Preserving Technique against Membership Inference Attacks

inferredcites →https://doi.org/10.48550/arxiv.2110.08324openalex-snapshot

Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture

inferredcites →https://doi.org/10.48550/arxiv.2202.05469openalex-snapshot

Privacy-preserving Generative Framework Against Membership Inference Attacks

inferredcites →https://doi.org/10.48550/arxiv.2202.06539openalex-snapshot

Deduplicating Training Data Mitigates Privacy Risks in Language Models

inferredcites →https://doi.org/10.48550/arxiv.2203.05212openalex-snapshot

Membership Privacy Protection for Image Translation Models via Adversarial Knowledge Distillation

inferredcites →https://doi.org/10.48550/arxiv.2210.10750openalex-snapshot

Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries

inferredcites →https://doi.org/10.52202/075280-1921openalex-snapshot

Students Parrot Their Teachers: Membership Inference on Model Distillation

inferredcites →https://doi.org/10.5555/1756006.1953019openalex-snapshot

Learnability, Stability and Uniform Convergence

inferredcites →https://doi.org/10.56553/popets-2023-0024openalex-snapshot

MIAShield: Defending Membership Inference Attacks via Preemptive Exclusion of Members

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