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

Milad Nasr; Reza Shokri; Amir Houmansadr

doi:https://doi.org/10.1109/sp.2019.00065

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

2019 · 1,624 citations · 2 from inside this corpus

Milad Nasr, Reza Shokri, Amir Houmansadr

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.

13 of 13 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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A review of applications in 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→Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…Legacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Machine learning a probabilistic perspectiveLegacy 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…→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→The Algorithmic Foundations of Differential PrivacyLegacy 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
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
Comprehensive Privacy Analysis of Deep Learning: Passive and Active White-box Inference A…→Federated Learning: Strategies for Improving Communication EfficiencyLegacy 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
The Algorithmic Foundations of Differential Privacy→Calibrating Noise to Sensitivity in Private Data AnalysisLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→Machine learning a probabilistic perspectiveLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→The Algorithmic Foundations of Differential PrivacyLegacy 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→The Algorithmic Foundations of Differential PrivacyLegacy 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
Deep Learning with Differential Privacy→The Algorithmic Foundations of Differential PrivacyLegacy 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→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
Membership Inference Attacks Against Machine Learning Models→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
A review of applications in federated learning→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
A review of applications in federated learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
A review of applications in federated learning→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Federated Learning: Strategies for Improving Communication EfficiencyLegacy edge — source detail unavailable
A survey on security and privacy of federated learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
The Algorithmic Foundations of Differential Privacy→Communication-Efficient Learning of Deep Networks from Decentralized\n DataLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
Machine learning a probabilistic perspective	2012	9,324
Calibrating Noise to Sensitivity in Private Data Analysis	2006	7,210
Deep Learning with Differential Privacy	2016	6,220
Communication-Efficient Learning of Deep Networks from Decentralized\n Data	2016	5,645
Communication-Efficient Learning of Deep Networks from Decentralized Data	2016	5,190
The Algorithmic Foundations of Differential Privacy	2014	4,578
Membership Inference Attacks Against Machine Learning Models	2017	4,490
The Algorithmic Foundations of Differential Privacy	2013	4,058
Federated Learning: Strategies for Improving Communication Efficiency	2016	3,050
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
A review of applications in federated learning	2020	1,502
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
Stochastic Gradient Optimization Techniques	Computer Science

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supports3 author record(s) attached.
supports65 reference(s) recorded.
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