Who Cited It

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.

Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …Dropout: a simple way to prevent neural networks from overfitting (2014)Dropout: a simple way to prev…Distilling the Knowledge in a Neural Network (2015)Distilling the Knowledge in a…Calibrating Noise to Sensitivity in Private Data Analysis (2006)Calibrating Noise to Sensitiv…Deep Learning with Differential Privacy (2016)Deep Learning with Differenti…Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015)Model Inversion Attacks that …Privacy-Preserving Deep Learning (2015)Privacy-Preserving Deep Learn…Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …The future of digital health with federated learning (2020)The future of digital health …Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …On the Opportunities and Risks of Foundation Models (2021)On the Opportunities and Risk…Adversarial Examples: Attacks and Defenses for Deep Learning (2019)Adversarial Examples: Attacks…Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A… (2019)Comprehensive Privacy Analysi…Deep Leakage from Gradients (2020)Deep Leakage from GradientsSecure, privacy-preserving and federated machine learning in medical imaging (2020)Secure, privacy-preserving an…A survey on security and privacy of federated learning (2020)A survey on security and priv…
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.
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What this paper cites, inside the corpus

PaperYearCited
Dropout: a simple way to prevent neural networks from overfitting201434,236
Distilling the Knowledge in a Neural Network201514,099
Calibrating Noise to Sensitivity in Private Data Analysis20067,210
Deep Learning with Differential Privacy20166,220
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures20152,885
Privacy-Preserving Deep Learning20152,325

What cites it, inside the corpus

PaperYearCited
Advances and Open Problems in Federated Learning20205,383
Practical Secure Aggregation for Privacy-Preserving Machine Learning20173,744
The future of digital health with federated learning20202,933
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
On the Opportunities and Risks of Foundation Models20212,265
Adversarial Examples: Attacks and Defenses for Deep Learning20191,786
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…20191,624
Deep Leakage from Gradients20201,532
Secure, privacy-preserving and federated machine learning in medical imaging20201,454
A survey on security and privacy of federated learning20201,393

Links

DOI · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Adversarial Robustness in Machine LearningComputer Science
Cryptography and Data SecurityComputer 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.

sha256 648f8491aaceaa34…