Fetching the paper…
Reading the bibliography…
Membership inference (MI) attacks affect user privacy by inferring whether given data samples have been used to train a target learning model, e.g., a deep neural network.
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola, “Integrating structured biological data by kernel maximum mean discrepancy,” Bioinformatics , vol. 22, no. 14, pp. e49–e57, 2006
2006
Earlier work this paper cites.
C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating noise to sensitivity in private data analysis,” in Theory of cryptography conference . Springer, 2006, pp. 265–284
2006
Earlier work this paper cites.
N. Homer, S. Szelinger, M. Redman, D. Duggan, W. Tembe, J. Muehling, J. V. Pearson, D. A. Stephan, S. F. Nelson, and D. W. Craig, “Resolving individuals contributing trace amounts of dna to highly complex mixtures using high-density snp genotyping microarrays,” PLoS genetics , vol. 4, no. 8, 2008
2008
Earlier work this paper cites.
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona, “Caltech-UCSD Birds 200,” California Institute of Technology, Tech. Rep. CNS-TR-2010-001, 2010
2010
Earlier work this paper cites.
P. Burlina, D. E. Freund, B. Dupas, and N. Bressler, “Automatic screening of age-related macular degeneration and retinal abnormalities,” in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society . IEEE, 2011, pp. 3962–3966
2011
Earlier work this paper cites.
K. Chaudhuri, C. Monteleoni, and A. D. Sarwate, “Differentially private empirical risk minimization.” Journal of Machine Learning Research , vol. 12, no. 3, 2011
2011
Earlier work this paper cites.
A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, “A kernel two-sample test,” Journal of Machine Learning Research , vol. 13, no. Mar, pp. 723–773, 2012
2012
Earlier work this paper cites.
A. Graves, A.-r. Mohamed, and G. Hinton, “Speech recognition with deep recurrent neural networks,” in 2013 IEEE international conference on acoustics, speech and signal processing . IEEE, 2013, pp. 6645–6649
2013
Earlier work this paper cites.
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 USENIX Security Symposium , 2014
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in neural information processing systems , 2014, pp. 2672–2680
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson, “How transferable are features in deep neural networks?” in Advances in neural information processing systems , 2014, pp. 3320–3328
2014
Earlier work this paper cites.
G. Ateniese, L. V. Mancini, A. Spognardi, A. Villani, D. Vitali, and G. Felici, “Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers,” International Journal of Security and Networks , vol. 10, no. 3, 2015
2015
Earlier work this paper cites.
Y. Cao and J. Yang, “Towards making systems forget with machine unlearning,” in Proceedings of the 2015 IEEE Symposium on Security and Privacy , 2015
2015
Earlier work this paper cites.
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 , ser. CCS ’15. New York, NY, USA: Association for Computing Machinery, 2015, p. 1322–1333. [Online]. Available: https://doi.org/10.1145/2810103.2813677
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security , 2015, pp. 1310–1321
2015
Earlier work this paper cites.
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
Cited alongside, same era.
2016
Cited alongside, same era.
T. Chen, I. Goodfellow, and J. Shlens, “Net2net: Accelerating learning via knowledge transfer,” 2016
2016
Cited alongside, same era.
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
Cited alongside, same era.
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 CCS , 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
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
Later among the works it cites.
B. Wang and N. Z. Gong, “Stealing hyperparameters in machine learning,” in 2018 IEEE Symposium on Security and Privacy (SP) . IEEE, 2018, pp. 36–52
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. N. Kather, C.-A. Weis, F. Bianconi, S. M. Melchers, L. R. Schad, T. Gaiser, A. Marx, and F. G. Z”ollner, “Multi-class texture analysis in colorectal cancer histology,” Scientific reports , vol. 6, p. 27988, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
S.-M. Moosavi-Dezfooli, A. Fawzi, and P. Frossard, “Deepfool: a simple and accurate method to fool deep neural networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2574–2582
2016
Cited alongside, same era.
N. Papernot, P. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The limitations of deep learning in adversarial settings,” in 2016 IEEE European symposium on security and privacy (EuroS&P) . IEEE, 2016, pp. 372–387
2016
Cited alongside, same era.
F. Tramèr, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing machine learning models via prediction apis,” in USENIX Security Symposium , 2016, pp. 601–618
2016
Cited alongside, same era.
P. M. Burlina, N. Joshi, M. Pekala, K. D. Pacheco, D. E. Freund, and N. M. Bressler, “Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks,” JAMA ophthalmology , vol. 135, no. 11, pp. 1170–1176, 2017
2017
Cited alongside, same era.
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.
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.
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
Later among the works it cites.
B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning transferable architectures for scalable image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8697–8710
2018
Later among the works it cites.
J. Hayes, L. Melis, G. Danezis, and E. De Cristofaro, “Logan: Membership inference attacks against generative models,” Proceedings on Privacy Enhancing Technologies , vol. 2019, no. 1, pp. 133–152, 2019
2019
Later among the works it cites.
R. Iyengar, J. P. Near, D. Song, O. Thakkar, A. Thakurta, and L. Wang, “Towards practical differentially private convex optimization,” in 2019 IEEE Symposium on Security and Privacy (SP) . IEEE, 2019, pp. 299–316
2019
Later among the works it cites.
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
Later among the works it cites.
2019
Later among the works it cites.
——, “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
Later among the works it cites.
S. J. Oh, B. Schiele, and M. Fritz, “Towards reverse-engineering black-box neural networks,” in Explainable AI: Interpreting, Explaining and Visualizing Deep Learning . Springer, 2019
2019
Later among the works it cites.
A. Sablayrolles, M. Douze, C. Schmid, Y. Ollivier, and H. Jégou, “White-box vs black-box: Bayes optimal strategies for membership inference,” in International Conference on Machine Learning , 2019, pp. 5558–5567
2019
Later among the works it cites.
A. Salem, Y. Zhang, M. Humbert, M. Fritz, and M. Backes, “Ml-leaks: Model and data independent membership inference attacks and defenses on machine learning models,” in Network and Distributed Systems Security Symposium 2019 . Internet Society, 2019
2019
Later among the works it cites.
L. Song, R. Shokri, and P. Mittal, “Privacy risks of securing machine learning models against adversarial examples,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security , 2019, pp. 241–257
2019
Later among the works it cites.
2020
Later among the works it cites.