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Cited by 4

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Advancing membership inference attacks: The present and the future

Li, Zheng, Zhang, Yang·2025·Security and Safety
DOI 10.1051/sands/2024017
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Research works printed but not linked: 19

  1. Shokri R, Stronati M and Song C et al. Membership inference attacks against machine learning models. In: IEEE Symposium on Security and Privacy (S &P), IEEE, 2017, 3–18. · DOI · Paper page
  2. Salem A, Zhang Y and Humbert M et al. ML-leaks: model and data independent membership inference attacks and defenses on machine learning models. In: Network and Distributed System Security Symposium (NDSS), Internet Society, 2019. · DOI · Paper page
  3. He X, Wen R and Wu Y et al. Node-level membership inference attacks against graph neural networks. CoRR abs/2102.05429, 2021. · arXiv
  4. Li Z and Zhang Y. Membership leakage in label-only exposures. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), ACM, 2021,880–895. · DOI
  5. Liu Y, Zhao Z and Backes M et al. Membership inference attacks by exploiting loss trajectory. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), ACM, 2022, 2085–2098. · DOI
  6. He X, Li Z and Xu W et al. Membership-doctor: comprehensive assessment of membership inference against machine learning models. CoRR abs/2208.10445, 2022. · arXiv
  7. Wu Y, Yu N and Li Z et al. Membership inference attacks against text-to-image generation models. CoRR https://arxiv.org/abs/2210.00968, 2022. · arXiv
  8. Nasr M, Shokri R and Houmansadr A. Comprehensive privacy analysis of deep learning: passive and active white-box inference attacks against centralized and federated learning. In: IEEE Symposium on Security and Privacy (S &P), IEEE, 2019, 1021–1035. · DOI · Paper page
  9. Leino K and Fredrikson M. Stolen memories: leveraging model memorization for calibrated white-box membership inference. In: USENIX Security Symposium (USENIX Security), USENIX, 2020, 1605–1622.
  10. Li J, Li N and Ribeiro B. Membership inference attacks and defenses in classification models. In: ACM Conference on Data and Application Security and Privacy (CODASPY), ACM, 2021, 5–16. · DOI
  11. Song L and Mittal P. Systematic evaluation of privacy risks of machine learning models. In: USENIX Security Symposium (USENIX Security). USENIX, 2021.
  12. Carlini N, Chien S and Nasr M et al. Membership inference attacks from first principles. In: IEEE Symposium on Security and Privacy (S &P), IEEE, 2022, 1897–1914. · DOI
  13. Li H, Li Z and Wu S et al. SeqMIA: sequential-metric based membership inference attack. CoRR https://arxiv.org/abs/2407.15098, 2024. · DOI
  14. Choquette Choo CA, Tramèr F and Carlini N et al. Label-only membership inference attacks. In: International Conference on Machine Learning (ICML), PMLR, 2021, 1964–1974.
  15. Wu Y, Qiu H and Guo S et al. You only query once: an efficient label-only membership inference attack. In: The Twelfth International Conference on Learning Representations, 2024.
  16. Dwork C, McSherry F and Nissim K et al. Calibrating noise to sensitivity in private data analysis. In: Theory of Cryptography Conference (TCC), Springer, 2006, 265–284. · DOI · Paper page
  17. Iyengar R, Near JP and Song DX et al. Towards practical differentially private convex optimization. In: IEEE Symposium on Security and Privacy (S &P), IEEE, 2019, 299–316. · DOI
  18. Truex S, Liu L and Emre Gursoy M et al. Towards demystifying membership inference attacks. CoRR https://arxiv.org/abs/1807.09173, 2018. · arXiv
  19. Jia J, Salem A and Backes M et al. MemGuard: defending against black-box membership inference attacks via adversarial examples. In: ACM SIGSAC Conference on Computer and Communications Security (CCS), ACM, 2019, 259–274. · DOI

Cited by

Metadata

Authors
Li, Z., & Zhang, Y.
ORCID
Venue
Security and Safety
Publisher
EDP Sciences
Published
2025
Volume
4
Pages
2024017
ISSN
2826-1275
DOI
10.1051/sands/2024017
Cited by
4
References
1
Licence
creativecommons.org/licenses/by/4.0 (vor, 29 d)

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