Membership Inference Attacks Against Machine Learning Models

Reza Shokri; Marco Stronati; Congzheng Song; Vitaly Shmatikov

doi:https://doi.org/10.1109/sp.2017.41

Membership Inference Attacks Against Machine Learning Models

2017 · 4,490 citations · 10 from inside this corpus

Reza Shokri, Marco Stronati low, Congzheng Song, Vitaly Shmatikov

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

16 of 16 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.

this paper works it cites works citing it node size = global citations · hover for the full title

Citation edge evidence

Each row is directed from the citing work to the cited work. Source status remains readable without the interactive graph.

Advances and Open Problems in Federated Learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
The future of digital health with federated learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Deep Leakage from Gradients→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Secure, privacy-preserving and federated machine learning in medical imaging→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Membership Inference Attacks Against Machine Learning Models→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Distilling the Knowledge in a Neural Network→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Deep Learning with Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Deep Learning with Differential Privacy→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Deep Learning with Differential Privacy→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Adversarial Examples: Attacks and Defenses for Deep Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Deep Leakage from Gradients→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
The future of digital health with federated learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
The future of digital health with federated learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Dropout: a simple way to prevent neural networks from overfittingLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Secure, privacy-preserving and federated machine learning in medical imaging→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…Legacy edge — source detail unavailable
A survey on security and privacy of federated learning→Federated Learning in Mobile Edge Networks: A Comprehensive SurveyLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Distilling the Knowledge in a Neural NetworkLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
On the Opportunities and Risks of Foundation Models→Model Inversion Attacks that Exploit Confidence Information and Basic CountermeasuresLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Dropout: a simple way to prevent neural networks from overfitting	2014	34,236
Distilling the Knowledge in a Neural Network	2015	14,099
Calibrating Noise to Sensitivity in Private Data Analysis	2006	7,210
Deep Learning with Differential Privacy	2016	6,220
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures	2015	2,885
Privacy-Preserving Deep Learning	2015	2,325

What cites it, inside the corpus

Paper	Year	Cited
Advances and Open Problems in Federated Learning	2020	5,383
Practical Secure Aggregation for Privacy-Preserving Machine Learning	2017	3,744
The future of digital health with federated learning	2020	2,933
Federated Learning in Mobile Edge Networks: A Comprehensive Survey	2020	2,538
On the Opportunities and Risks of Foundation Models	2021	2,265
Adversarial Examples: Attacks and Defenses for Deep Learning	2019	1,786
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…	2019	1,624
Deep Leakage from Gradients	2020	1,532
Secure, privacy-preserving and federated machine learning in medical imaging	2020	1,454
A survey on security and privacy of federated learning	2020	1,393

Links

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Topics

Privacy-Preserving Technologies in Data	Computer Science
Adversarial Robustness in Machine Learning	Computer Science
Cryptography and Data Security	Computer Science

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complete

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supports4 author record(s) attached.
supports54 reference(s) recorded.
supportsThe DOI's year agrees with the publication year.
supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:44+00:00.

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