The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning
Open accessAbstract
Deep Learning has recently become hugely popular in machine learning for its ability to solve end-to-end learning systems, in which the features and the classifiers are learned simultaneously, providing significant improvements in classification accuracy in the presence of highly-structured and large databases.
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In the citing paper
“By definition, FL systems avoid sharing healthcare data among participating institutions.”
From The future of digital health with federated learning (2020) · cites this paper as [63]
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In the citing paper
“It is therefore possible to reconstruct parts of the training data from the algorithm weights themselves on a decentralized node [52][53][54] .”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [54]
In the citing paper
“Attackers can recover data from gradients uploaded by customers [4].”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“But their experiments confirmed that GAN-based approaches might not work well when the selected privacy parameter was smaller than 10 , which is the upper bound of privacy parameter in our experiment, and we can achieve the accuracy of 97% .”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“There are studies discussing potential risks of information leakage in FL [4,66] in which attackers may infer customers' private data from gradients.”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“Hitaj et al. [4] demonstrated that a curious server could obtain the confidential information using the generative adversarial network if gradients were protected by a large privacy budget in the collaborative learning.”
From Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices (2021) · cites this paper as [4]
In the citing paper
“A combination of both previously cited defenses [60] identifies negatively impacting updates from clients.”
From Vulnerabilities in federated learning (2021) · cites this paper as [60]
In the citing paper
“The reason why gradients can cause privacy leakage is that the gradients are derived from the participants' private training data, and a learning model can be considered as a representation of the high-level statistics of the dataset it was trained on [75].”
From Privacy and Robustness in Federated Learning: Attacks and Defenses (2024) · cites this paper as [75]
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Metadata
- Authors
- Hitaj, B., Ateniese, G., & Perez-Cruz, F.
- Affiliations
- Stevens Institute of Technology (US)
- Stevens Institute of Technology, Hoboken, NJ, USA
- Venue
- Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security
- Publisher
- ACM
- Published
- 2017-10-30
- Pages
- 603-618
- ISSN
- 1543-7221 (ISSN-L)
- OpenAlex
- W2591882872
- Semantic Scholar
- 44a97f4eaaefaf5338f8aed2913d5debb2459f7e
- Cited by
- 1,377
- References
- 16
- Citations by year
- 2026: 80 · 2025: 192 · 2024: 214 · 2023: 229 · 2022: 176 · 2021: 204 · 2020: 147 · 2019: 93 · 2018: 39 · 2017: 3
- FWCI
- 22.62
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- ACM SIGSAC Conference on Computer and Communications Security (CCS) (publishedVersion)
- IRIS Research product catalog (Sapienza University of Rome) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- generative adversarial networks, deep learning, end-to-end learning, representation learning, classifier training, structured data
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