Who Cited It

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.

Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …How to share a secret (1979)How to share a secretDeep Learning (2016)Deep LearningDeep Learning with Differential Privacy (2016)Deep Learning with Differenti…Differential Privacy (2006)Differential PrivacyCommunication-Efficient Learning of Deep Networks from Decentralized Data (2016)Communication-Efficient Learn…Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …The Algorithmic Foundations of Differential Privacy (2013)The Algorithmic Foundations o…Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015)Model Inversion Attacks that …Completeness theorems for non-cryptographic fault-tolerant distributed computation (1988)Completeness theorems for non…Robust De-anonymization of Large Sparse Datasets (2008)Robust De-anonymization of La…Privacy-Preserving Deep Learning (2015)Privacy-Preserving Deep Learn…Our Data, Ourselves: Privacy Via Distributed Noise Generation (2006)Our Data, Ourselves: Privacy …RAPPOR (2014)RAPPORThe dining cryptographers problem: Unconditional sender and recipient untraceability (1988)The dining cryptographers pro…Federated Machine Learning (2019)Federated Machine LearningAdvances and Open Problems in Federated Learning (2020)Advances and Open Problems in…Federated Learning: Challenges, Methods, and Future Directions (2020)Federated Learning: Challenge…Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Federated Learning with Non-IID Data (2018)Federated Learning with Non-I…A survey on federated learning (2021)A survey on federated learningPrivacy-Preserving Deep Learning via Additively Homomorphic Encryption (2017)Privacy-Preserving Deep Learn…Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge (2019)Client Selection for Federate…Federated Learning for Healthcare Informatics (2020)Federated Learning for Health…
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.
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What this paper cites, inside the corpus

PaperYearCited
How to share a secret197913,642
Deep Learning20168,952
Deep Learning with Differential Privacy20166,220
Differential Privacy20065,337
Communication-Efficient Learning of Deep Networks from Decentralized Data20165,190
Membership Inference Attacks Against Machine Learning Models20174,490
The Algorithmic Foundations of Differential Privacy20134,058
Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures20152,885
Completeness theorems for non-cryptographic fault-tolerant distributed computation19882,542
Robust De-anonymization of Large Sparse Datasets20082,349
Privacy-Preserving Deep Learning20152,325
Our Data, Ourselves: Privacy Via Distributed Noise Generation20061,826
RAPPOR20141,548
The dining cryptographers problem: Unconditional sender and recipient untraceability19881,419

What cites it, inside the corpus

PaperYearCited
Federated Machine Learning20196,228
Advances and Open Problems in Federated Learning20205,383
Federated Learning: Challenges, Methods, and Future Directions20204,992
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Federated Learning with Non-IID Data20181,918
A survey on federated learning20211,847
Privacy-Preserving Deep Learning via Additively Homomorphic Encryption20171,665
Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge20191,528
Federated Learning for Healthcare Informatics20201,497

Links

DOI · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Cryptography and Data SecurityComputer Science
Stochastic Gradient Optimization TechniquesComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

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

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:46+00:00.

sha256 bba2969b3567609a…