ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

M. Backes, M. Humbert, J. Pang, and Y. Zhang, “walk2friends: Inferring Social Links from Mobility Profiles,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2017, pp. 1943–1957

1957

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 , 2008

2008

Earlier work this paper cites.

Z. Erkin, M. Franz, J. Guajardo, S. Katzenbeisser, I. Lagendijk, and T. Toft, “Privacy-Preserving Face Recognition,” Symposium on Privacy Enhancing Technologies Symposium , 2009

2009

Earlier work this paper cites.

Y. Huang, L. Malka, D. Evans, and J. Katz, “Efficient Privacy-Preserving Biometric Identification,” in Proceedings of the 2011 Network and Distributed System Security Symposium (NDSS) . Internet Society, 2011

2011

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 Proceedings of the 2014 USENIX Security Symposium (USENIX Security) . USENIX, 2014, pp. 17–32

2014

Earlier work this paper cites.

Y. Vorobeychik and B. Li, “Optimal Randomized Classification in Adversarial Settings,” in Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems (AAMAS) , 2014, pp. 485–492

2014

Earlier work this paper cites.

R. Bost, R. A. Popa, S. Tu, and S. Goldwasser, “Machine Learning Classification over Encrypted Data,” in Proceedings of the 2015 Network and Distributed System Security Symposium (NDSS) . Internet Society, 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 2015 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2015, pp. 1322–1333

2015

Earlier work this paper cites.

I. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and Harnessing Adversarial Examples,” in Proceedings of the 2015 International Conference on Learning Representations (ICLR) , 2015

2015

Earlier work this paper cites.

B. Li and Y. Vorobeychik, “Scalable Optimization of Randomized Operational Decisions in Adversarial Classification Settings,” in Proceedings of the 2015 International Conference on Artificial Intelligence and Statistics (AISTATS) . PMLR, 2015, pp. 599–607

2015

Earlier work this paper cites.

M. Backes, P. Berrang, M. Humbert, and P. Manoharan, “Membership Privacy in MicroRNA-based Studies,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2016, pp. 319–330

2016

Earlier work this paper cites.

N. Dowlin, R. Gilad-Bachrach, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, “CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy,” in Proceedings of the 2016 International Conference on Machine Learning (ICML) . JMLR, 2016, pp. 201–210

2016

Earlier work this paper cites.

N. Papernot, P. D. McDaniel, S. Jha, M. Fredrikson, Z. B. Celik, and A. Swami, “The Limitations of Deep Learning in Adversarial Settings,” in Proceedings of the 2016 IEEE European Symposium on Security and Privacy (Euro S&P) . IEEE, 2016, pp. 372–387

2016

Earlier work this paper cites.

F. Tramér, F. Zhang, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing Machine Learning Models via Prediction APIs,” in Proceedings of the 2016 USENIX Security Symposium (USENIX Security) . USENIX, 2016, pp. 601–618

2016

Earlier work this paper cites.

D. Yang, D. Zhang, and B. Qu, “Participatory Cultural Mapping Based on Collective Behavior Data in Location-Based Social Networks,” ACM Transactions on Intelligent Systems and Technology , 2016

2016

Earlier work this paper cites.

M. Backes, P. Berrang, M. Bieg, R. Eils, C. Herrmann, M. Humbert, and I. Lehmann, “Identifying Personal DNA Methylation Profiles by Genotype Inference,” in Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P) . IEEE, 2017, pp. 957–976

2017

Earlier work this paper cites.

K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical Secure Aggregation for Privacy-Preserving Machine Learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security (CCS) . ACM, 2017, pp. 1175–1191

2017

Earlier work this paper cites.

X. Cao and N. Z. Gong, “Mitigating Evasion Attacks to Deep Neural Networks via Region-based Classification,” in Proceedings of the 2017 Annual Computer Security Applications Conference (ACSAC) . ACM, 2017, pp. 278–287

2017

Cited alongside, same era.

N. Carlini and D. Wagner, “Towards Evaluating the Robustness of Neural Networks,” in Proceedings of the 2017 IEEE Symposium on Security and Privacy (S&P) . IEEE, 2017, pp. 39–57

2017

Cited alongside, same era.

A. Gascón, P. Schoppmann, B. Balle, M. Raykova, J. Doerner, S. Zahur, and D. Evans, “Privacy-Preserving Distributed Linear Regression on High-Dimensional Data,” Symposium on Privacy Enhancing Technologies Symposium , 2017

2017

Cited alongside, same era.

J. Hayes, L. Melis, G. Danezis, and E. D. Cristofaro, “LOGAN: Evaluating Privacy Leakage of Generative Models Using Generative Adversarial Networks,” CoRR abs/1705.07663 , 2017

Original

2017

Cited alongside, same era.

L. Hanzlik, Y. Zhang, K. Grosse, A. Salem, M. Augustin, M. Backes, and M. Fritz, “MLCapsule: Guarded Offline Deployment of Machine Learning as a Service,” CoRR abs/1808.00590 , 2018

Original

2018

Closest in time.

T. Hunt, C. Song, R. Shokri, V. Shmatikov, and E. Witchel, “Chiron: Privacy-preserving Machine Learning as a Service,” CoRR abs/1803.05961 , 2018

Original

2018

Closest in time.

M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li, “Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning,” in Proceedings of the 2018 IEEE Symposium on Security and Privacy (S&P) . IEEE, 2018

2018

Closest in time.

J. Jia and N. Z. Gong, “AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning,” in Proceedings of the 2018 USENIX Security Symposium (USENIX Security) . USENIX, 2018

2018

Closest in time.