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Machine learning (ML) models are vulnerable to membership inference attacks (MIAs), which determine whether a given input is used for training the target model.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in 2022 IEEE Symposium on Security and Privacy (SP) . IEEE, 2022, pp. 1897–1914
1914
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C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in International Conference on Machine Learning . PMLR, 2021, pp. 1964–1974
1974
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A. Krogh and J. A. Hertz, “A simple weight decay can improve generalization,” in Advances in neural information processing systems , 1992, pp. 950–957
1992
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R. Caruana, S. Lawrence, and L. Giles, “Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping,” Advances in neural information processing systems , pp. 402–408, 2001
2001
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A. Krizhevsky, G. Hinton et al. , “Learning multiple layers of features from tiny images,” 2009
2009
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E. W. Ngai, Y. Hu, Y. H. Wong, Y. Chen, and X. Sun, “The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature,” Decision support systems , vol. 50, no. 3, pp. 559–569, 2011
2011
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M. Fredrikson, E. Lantz, S. Jha, S. Lin, D. Page, and T. Ristenpart, “Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing,” in 23rd { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 14) , 2014, pp. 17–32
2014
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N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: a simple way to prevent neural networks from overfitting,” The journal of machine learning research , vol. 15, no. 1, pp. 1929–1958, 2014
2014
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M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security , 2015, pp. 1322–1333
2015
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K. Kourou, T. P. Exarchos, K. P. Exarchos, M. V. Karamouzis, and D. I. Fotiadis, “Machine learning applications in cancer prognosis and prediction,” Computational and structural biotechnology journal , vol. 13, pp. 8–17, 2015
2015
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2015
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M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security , 2016, pp. 308–318
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
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I. Kemelmacher-Shlizerman, S. M. Seitz, D. Miller, and E. Brossard, “The megaface benchmark: 1 million faces for recognition at scale,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 4873–4882
2016
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2016
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
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F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in 25th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 16) , 2016, pp. 601–618
2016
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N. Carlini and D. Wagner, “Towards evaluating the robustness of neural networks,” in 2017 ieee symposium on security and privacy (sp) . IEEE, 2017, pp. 39–57
2017
Cited alongside, same era.
2017
Cited alongside, same era.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 4700–4708
2017
Cited alongside, same era.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in 2017 IEEE Symposium on Security and Privacy (SP) . IEEE, 2017, pp. 3–18
2017
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
B. Jayaraman, L. Wang, K. Knipmeyer, Q. Gu, and D. Evans, “Revisiting membership inference under realistic assumptions,” Proceedings on Privacy Enhancing Technologies , vol. 2021, no. 2, 2021
2021
Later among the works it cites.
Y. Kaya and T. Dumitras, “When does data augmentation help with membership inference attacks?” in International Conference on Machine Learning . PMLR, 2021, pp. 5345–5355
2021
Later among the works it cites.
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K. Ganju, Q. Wang, W. Yang, C. A. Gunter, and N. Borisov, “Property inference attacks on fully connected neural networks using permutation invariant representations,” in Proceedings of the 2018 ACM SIGSAC conference on computer and communications security , 2018, pp. 619–633
2018
Cited alongside, same era.
M. Nasr, R. Shokri, and A. Houmansadr, “Machine learning with membership privacy using adversarial regularization,” in Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security , 2018, pp. 634–646
2018
Cited alongside, same era.
M. A. Rahman, T. Rahman, R. Laganière, N. Mohammed, and Y. Wang, “Membership inference attack against differentially private deep learning model.” Trans. Data Priv. , vol. 11, no. 1, pp. 61–79, 2018
2018
Cited alongside, same era.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in 2018 IEEE 31st Computer Security Foundations Symposium (CSF) . IEEE, 2018, pp. 268–282
2018
Cited alongside, same era.
X. Zhang, X. Zhou, M. Lin, and J. Sun, “Shufflenet: An extremely efficient convolutional neural network for mobile devices,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 6848–6856
2018
Cited alongside, same era.
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Membership inference attacks against generative models,” in Proceedings on Privacy Enhancing Technologies (PoPETs) , vol. 2019, no. 1. De Gruyter, 2019, pp. 133–152
2019
Cited alongside, same era.
J. Jia, A. Salem, M. Backes, Y. Zhang, and N. Z. Gong, “Memguard: Defending against black-box membership inference attacks via adversarial examples,” in Proceedings of the 2019 ACM SIGSAC conference on computer and communications security , 2019, pp. 259–274
2019
Cited alongside, same era.
——, “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) . IEEE, 2019, pp. 739–753
2019
Cited alongside, same era.
J. Li, N. Li, and B. Ribeiro, “Membership inference attacks and defenses in classification models,” in Proceedings of the Eleventh ACM Conference on Data and Application Security and Privacy , 2021, pp. 5–16
2021
Later among the works it cites.
Z. Li and Y. Zhang, “Membership leakage in label-only exposures,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , 2021, pp. 880–895
2021
Later among the works it cites.
N. Papernot, A. Thakurta, S. Song, S. Chien, and Ú. Erlingsson, “Tempered sigmoid activations for deep learning with differential privacy,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 10, 2021, pp. 9312–9321
2021
Later among the works it cites.
V. Shejwalkar and A. Houmansadr, “Membership privacy for machine learning models through knowledge transfer,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 35, no. 11, pp. 9549–9557, May 2021
2021
Later among the works it cites.
L. Song and P. Mittal, “Systematic evaluation of privacy risks of machine learning models,” in 30th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 21) , 2021
2021
Later among the works it cites.
J.-B. Truong, P. Maini, R. J. Walls, and N. Papernot, “Data-free model extraction,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 4771–4780
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Zhang, Z. Ren, Z. Wang, P. Ren, Z. Chen, P. Hu, and Y. Zhang, “Membership inference attacks against recommender systems,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , 2021, pp. 864–879
2021
Later among the works it cites.
Z. Zhang, M. Chen, M. Backes, Y. Shen, and Y. Zhang, “Inference attacks against graph neural networks,” in USENIX Security Symposium (USENIX Security). USENIX , vol. 2022, 2021, p. 13
2021
Later among the works it cites.
X. Tang, S. Mahloujifar, L. Song, V. Shejwalkar, M. Nasr, A. Houmansadr, and P. Mittal, “Mitigating membership inference attacks by { \{ Self-Distillation } \} through a novel ensemble architecture,” in 31st USENIX Security Symposium (USENIX Security 22) , 2022, pp. 1433–1450
2022
Later among the works it cites.
F. Tramèr, R. Shokri, A. San Joaquin, H. Le, M. Jagielski, S. Hong, and N. Carlini, “Truth serum: Poisoning machine learning models to reveal their secrets,” in Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security , 2022, p. 2779–2792
2022
Later among the works it cites.
H. Liu, J. Jia, W. Qu, and N. Z. Gong, “Encodermi: Membership inference against pre-trained encoders in contrastive learning,” in Proceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security , 2021, pp. 2081–2095
2095
Closest in time.