Practical Secure Aggregation for Privacy-Preserving Machine Learning

Keith Bonawitz; Vladimir Ivanov; Ben Kreuter; Antonio Marcedone; H. Brendan McMahan; Sarvar Patel; Daniel Ramage; Aaron Segal; Karn Seth

doi:https://doi.org/10.1145/3133956.3133982

Practical Secure Aggregation for Privacy-Preserving Machine Learning

2017 · 3,744 citations · 9 from inside this corpus

Keith Bonawitz low, Vladimir Ivanov, Ben Kreuter low, Antonio Marcedone low, H. Brendan McMahan, Sarvar Patel low, Daniel Ramage, Aaron Segal, Karn Seth low

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.

23 of 23 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.

Federated Machine Learning→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Federated Learning: Challenges, Methods, and Future Directions→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy 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
Federated Learning with Non-IID Data→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
A survey on federated learning→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Practical Secure Aggregation for Privacy-Preserving Machine LearningLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→How to share a secretLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Deep LearningLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Differential PrivacyLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→The Algorithmic Foundations of Differential PrivacyLegacy 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→Completeness theorems for non-cryptographic fault-tolerant distributed computationLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→Robust De-anonymization of Large Sparse DatasetsLegacy 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→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→RAPPORLegacy edge — source detail unavailable
Practical Secure Aggregation for Privacy-Preserving Machine Learning→The dining cryptographers problem: Unconditional sender and recipient untraceabilityLegacy edge — source detail unavailable
RAPPOR→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
Completeness theorems for non-cryptographic fault-tolerant distributed computation→How to share a secretLegacy edge — source detail unavailable
The Algorithmic Foundations of Differential Privacy→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
The Algorithmic Foundations of Differential Privacy→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
The Algorithmic Foundations of Differential Privacy→Differential PrivacyLegacy edge — source detail unavailable
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures→Robust De-anonymization of Large Sparse DatasetsLegacy edge — source detail unavailable
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures→Differential PrivacyLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→The Algorithmic Foundations of Differential PrivacyLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning→Differential PrivacyLegacy edge — source detail unavailable
Robust De-anonymization of Large Sparse Datasets→Differential PrivacyLegacy edge — source detail unavailable
Deep Learning with Differential Privacy→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy 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
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
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Learning with Non-IID Data→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Federated Machine Learning→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Federated Machine Learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Machine Learning→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy edge — source detail unavailable
Federated Machine Learning→Federated Learning with Non-IID DataLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→The Algorithmic Foundations of Differential PrivacyLegacy 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→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Advances and Open Problems in Federated Learning→Federated Machine LearningLegacy 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→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Membership Inference Attacks Against Machine Learning ModelsLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Federated Learning with Non-IID DataLegacy edge — source detail unavailable
Federated Learning in Mobile Edge Networks: A Comprehensive Survey→Federated Machine LearningLegacy edge — source detail unavailable
Federated Learning: Challenges, Methods, and Future Directions→Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Our Data, Ourselves: Privacy Via Distributed Noise GenerationLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Privacy-Preserving Deep LearningLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Client Selection for Federated Learning with Heterogeneous Resources in Mobile EdgeLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Federated Learning with Non-IID DataLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Federated Machine LearningLegacy edge — source detail unavailable
Federated Learning for Healthcare Informatics→Federated Learning in Mobile Edge Networks: A Comprehensive SurveyLegacy edge — source detail unavailable
A survey on federated learning→Deep Learning with Differential PrivacyLegacy edge — source detail unavailable
A survey on federated learning→Communication-Efficient Learning of Deep Networks from Decentralized DataLegacy edge — source detail unavailable
A survey on federated learning→Privacy-Preserving Deep Learning via Additively Homomorphic EncryptionLegacy edge — source detail unavailable
A survey on federated learning→Federated Machine LearningLegacy edge — source detail unavailable

What this paper cites, inside the corpus

Paper	Year	Cited
How to share a secret	1979	13,642
Deep Learning	2016	8,952
Deep Learning with Differential Privacy	2016	6,220
Differential Privacy	2006	5,337
Communication-Efficient Learning of Deep Networks from Decentralized Data	2016	5,190
Membership Inference Attacks Against Machine Learning Models	2017	4,490
The Algorithmic Foundations of Differential Privacy	2013	4,058
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures	2015	2,885
Completeness theorems for non-cryptographic fault-tolerant distributed computation	1988	2,542
Robust De-anonymization of Large Sparse Datasets	2008	2,349
Privacy-Preserving Deep Learning	2015	2,325
Our Data, Ourselves: Privacy Via Distributed Noise Generation	2006	1,826
RAPPOR	2014	1,548
The dining cryptographers problem: Unconditional sender and recipient untraceability	1988	1,419

What cites it, inside the corpus

Paper	Year	Cited
Federated Machine Learning	2019	6,228
Advances and Open Problems in Federated Learning	2020	5,383
Federated Learning: Challenges, Methods, and Future Directions	2020	4,992
Federated Learning in Mobile Edge Networks: A Comprehensive Survey	2020	2,538
Federated Learning with Non-IID Data	2018	1,918
A survey on federated learning	2021	1,847
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption	2017	1,665
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge	2019	1,528
Federated Learning for Healthcare Informatics	2020	1,497

Links

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Topics

Privacy-Preserving Technologies in Data	Computer Science
Cryptography and Data Security	Computer Science
Stochastic Gradient Optimization Techniques	Computer Science

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complete

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supports9 author record(s) attached.
supports56 reference(s) recorded.
neutralThe DOI carries no year to check against.
supportsA title is present.

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