Who Cited It

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

2015 · 2,885 citations · 12 from inside this corpus

Matt Fredrikson, Somesh Jha, Thomas Ristenpart

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.

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures (2015)Model Inversion Attacks that …Differential Privacy (2006)Differential PrivacyRobust De-anonymization of Large Sparse Datasets (2008)Robust De-anonymization of La…Deep Learning with Differential Privacy (2016)Deep Learning with Differenti…Advances and Open Problems in Federated Learning (2020)Advances and Open Problems in…Membership Inference Attacks Against Machine Learning Models (2017)Membership Inference Attacks …Practical Secure Aggregation for Privacy-Preserving Machine Learning (2017)Practical Secure Aggregation …Federated Learning in Mobile Edge Networks: A Comprehensive Survey (2020)Federated Learning in Mobile …Federated Learning With Differential Privacy: Algorithms and Performance Analysis (2020)Federated Learning With Diffe…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…Accessorize to a Crime (2016)Accessorize to a CrimeDeep Leakage from Gradients (2020)Deep Leakage from GradientsSecure, privacy-preserving and federated machine learning in medical imaging (2020)Secure, privacy-preserving an…
14 of 14 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
Differential Privacy20065,337
Robust De-anonymization of Large Sparse Datasets20082,349

What cites it, inside the corpus

PaperYearCited
Deep Learning with Differential Privacy20166,220
Advances and Open Problems in Federated Learning20205,383
Membership Inference Attacks Against Machine Learning Models20174,490
Practical Secure Aggregation for Privacy-Preserving Machine Learning20173,744
Federated Learning in Mobile Edge Networks: A Comprehensive Survey20202,538
Federated Learning With Differential Privacy: Algorithms and Performance Analysis20202,357
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
Accessorize to a Crime20161,596
Deep Leakage from Gradients20201,532
Secure, privacy-preserving and federated machine learning in medical imaging20201,454

Links

DOI · Open access copy · OpenAlex record

Topics

Privacy-Preserving Technologies in DataComputer Science
Adversarial Robustness in Machine LearningComputer Science
Autopsy Techniques and OutcomesMedicine

Is this record sound?

complete

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

  • supports3 author record(s) attached.
  • supports29 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 2026-09-04T03:58:48+00:00.

sha256 46f669157f96ea35…