The DOI matches its Crossref record; 47 of the 48 references it cites were found in a registry or an index.
Model inversion attacks against collaborative inference
Abstract
The prevalence of deep learning has drawn attention to the privacy protection of sensitive data. Various privacy threats have been presented, where an adversary can steal model owners' private data. Meanwhile, countermeasures have also been introduced to achieve privacy-preserving deep learning. However, most studies only focused on data privacy during training, and ignored privacy during inference.
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In the citing paper
“a number of works suggest5–7 that on its own, federated learning is an insufficient measure of privacy preservation.”
From Medical imaging deep learning with differential privacy (2021) · cites this paper as [7]
Citations
Cites
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Membership Inference Attacks Against Machine Learning Models
Shokri, R., Stronati, M., Song, C., & Shmatikov, V. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.41 · Cited by 4,754
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Research works printed but not linked: 14
- Ateniese Giuseppe, 2015, International Journal of Security and Networks
- Chilimbi Trishul, 2014, USENIX Symposium on Operating Systems Design and Implementation.
- Ganju Karan, 2018, ACM Conference on Computer and Communications Security.
- Glorot Xavier, 2010, Proceedings of the thirteenth international conference on artificial intelligence and statistics. 249--256
- Jamie Hayes Luca Melis George Danezis and Emiliano De Cristofaro. 2017. LOGAN: evaluating privacy leakage of generative models using generative adversarial networks. arXiv preprint arXiv:1705.07663 (2017). Jamie Hayes Luca Melis George Danezis and Emiliano De Cristofaro. 2017. LOGAN: evaluating privacy leakage of generative models using generative adversarial networks. arXiv preprint arXiv:1705.07663 (2017). · arXiv
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- Yunhui Long Vincent Bindschaedler Lei Wang Diyue Bu Xiaofeng Wang Haixu Tang Carl A Gunter and Kai Chen. 2018. Understanding Membership Inferences on Well-Generalized Learning Models. arXiv preprint arXiv:1802.04889 (2018). Yunhui Long Vincent Bindschaedler Lei Wang Diyue Bu Xiaofeng Wang Haixu Tang Carl A Gunter and Kai Chen. 2018. Understanding Membership Inferences on Well-Generalized Learning Models. arXiv preprint arXiv:1802.04889 (2018). · arXiv
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Cited by
Sorted by citations
End-to-end privacy preserving deep learning on multi-institutional medical imaging
Kaissis, G., Ziller, A., Passerat-Palmbach, J., Ryffel, T., Usynin, D., Trask, A., Lima, I., Mancuso, J., Jungmann, F., Steinborn, M.-M., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00337-8 · Cited by 513
“We note that while the data enjoys information-theoretic secrecy guarantees, the party requesting inference has access to the model’s predictions and can perform black-box membership inference 59 or model inversion attacks 60 .”
See through Gradients: Image Batch Recovery via GradInversion
Yin, H., Mallya, A., Vahdat, A., Álvarez, J. M., Kautz, J., & Molchanov, P. A. (2021) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr46437.2021.01607 · Cited by 519
Dreaming to Distill: Data-Free Knowledge Transfer via DeepInversion
Yin, H., Molchanov, P., Alvarez, J. M., Li, Z., Mallya, A., Hoiem, D., Jha, N. K., & Kautz, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00874 · Cited by 540
Vertical Federated Learning: Concepts, Advances, and Challenges
Liu, Y., Kang, Y., Zou, T., Pu, Y., He, Y., Ye, X., Ouyang, Y., Zhang, Y.-Q., & Yang, Q. (2024) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2024.3352628 · Cited by 496
“He et al. [95] proposed a black-box MI attack to learn features x B of the passive party B under the splitNN setting by training a shadow model ˆ G B to mimic the local model G B using auxiliary data.”
BackgroundMethodInfluentialA Comprehensive Survey on Poisoning Attacks and Countermeasures in Machine Learning
Tian, Z., Cui, L., Liang, J., & Yu, S. (2022) · ACM Computing Surveys · doi:10.1145/3551636 · Cited by 293
“The privacy attack could be defined as malicious attempts revealing training data, such as membership inference [109, 114, 125] and model inversion [34, 45, 146, 154, 161].”
Background
Metadata
- Authors
- He, Z., Zhang, T., & Lee, R. B.
- Affiliations
- Princeton University (US)
- Nanyang Technological University (SG)
- Venue
- Proceedings of the 35th Annual Computer Security Applications Conference
- Publisher
- ACM
- Published
- 2019-12-09
- Pages
- 148-162
- ISSN
- 1063-9527 (ISSN-L)
- OpenAlex
- W2989885118
- Cited by
- 313
- References
- 48
- Citations by year
- 2026: 30 · 2025: 81 · 2024: 62 · 2023: 49 · 2022: 36 · 2021: 39 · 2020: 16
- FWCI
- 8.70
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Annual Computer Security Applications Conference (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- model inversion attacks, collaborative inference, inference privacy, privacy-preserving deep learning, data privacy, adversarial attacks
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