Improved Membership Inference Attacks Against Language Classification Models

Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP). pp. 3–18. IEEE Computer Society, Los Alamitos, CA, USA (may 2017), https://doi.ieeecomputersociety.org/10.1109/SP.2017.41

2017

Earlier work this paper cites.

Nicolae, M.I., Sinn, M., Tran, M.N., Buesser, B., Rawat, A., Wistuba, M., Zantedeschi, V., Baracaldo, N., Chen, B., Ludwig, H., Molloy, I., Edwards, B.: Adversarial robustness toolbox v1.2.0. CoRR 1807.01069 (2018), https://arxiv.org/pdf/1807.01069

Original

2018

Earlier work this paper cites.

Salem, A., Zhang, Y., Humbert, M., Berrang, P., Fritz, M., Backes, M.: Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models. arXiv preprint arXiv:1806.01246 (2018)

Original

2018

Earlier work this paper cites.

Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: Analyzing the connection to overfitting. In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF). pp. 268–282 (2018)

2018

Earlier work this paper cites.

Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE symposium on security and privacy (SP). pp. 739–753. IEEE (2019)

2019

Earlier work this paper cites.

Kumar, S., Shokri, R.: Ml privacy meter: Aiding regulatory compliance by quantifying the privacy risks of machine learning. In: Workshop on Hot Topics in Privacy Enhancing Technologies (HotPETs) (2020)

2020

Earlier work this paper cites.

Song, C., Raghunathan, A.: Information leakage in embedding models. In: Proceedings of the 2020 ACM SIGSAC conference on computer and communications security. pp. 377–390 (2020)

2020

Cited alongside, same era.

Jagannatha, A., Rawat, B.P.S., Yu, H.: Membership inference attack susceptibility of clinical language models. arXiv preprint arXiv:2104.08305 (2021)

Original

2021

Cited alongside, same era.

Mahloujifar, S., Inan, H.A., Chase, M., Ghosh, E., Hasegawa, M.: Membership inference on word embedding and beyond. arXiv preprint arXiv:2106.11384 (2021)

Original

2021

Cited alongside, same era.

Shejwalkar, V., Inan, H.A., Houmansadr, A., Sim, R.: Membership inference attacks against nlp classification models. In: NeurIPS 2021 Workshop Privacy in Machine Learning (2021)

2021

Cited alongside, same era.

Hu, H., Salcic, Z., Sun, L., Dobbie, G., Yu, P.S., Zhang, X.: Membership inference attacks on machine learning: A survey. ACM Comput. Surv. 54(11s) (sep 2022), https://doi.org/10.1145/3523273

2022

Later among the works it cites.

Mireshghallah, F., Goyal, K., Uniyal, A., Berg-Kirkpatrick, T., Shokri, R.: Quantifying privacy risks of masked language models using membership inference attacks. arXiv preprint arXiv:2203.03929 (2022)

Original

2022

Later among the works it cites.

Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H.A., Kamath, G., Kulkarni, J., Lee, Y.T., Manoel, A., Wutschitz, L., Yekhanin, S., Zhang, H.: Differentially private fine-tuning of language models. In: ICLR (2022)

2022

Later among the works it cites.

Cai, Z., Tan, Y., Asif, M.S.: Ensemble-based blackbox attacks on dense prediction. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4045–4055 (2023)

2023

Closest in time.