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Data used to train machine learning (ML) models can be sensitive.
C. A. Choquette-Choo, F. Tramer, N. Carlini, and N. Papernot, “Label-only membership inference attacks,” in
1974
Earlier work this paper cites.
2007
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in
2015
Earlier work this paper cites.
L. S. Shapley,
2016
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. W. Koh and P. Liang, “Understanding black-box predictions via influence functions,” in
2017
Earlier work this paper cites.
Y. Long, V. Bindschaedler, and C. A. Gunter, “Towards measuring membership privacy,” in
2017
Earlier work this paper cites.
S. M. Lundberg and S.-I. Lee, “A unified approach to interpreting model predictions,” in
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
R. Shokri, M. Stronati, C. Song, and V. Shmatikov, “Membership inference attacks against machine learning models,” in
2017
Earlier work this paper cites.
Y. Adi, C. Baum, M. Cisse, B. Pinkas, and J. Keshet, “Turning your weakness into a strength: Watermarking deep neural networks by backdooring,” in
2018
Earlier work this paper cites.
A. Salem, Y. Zhang, M. Humbert, P. Berrang, M. Fritz, and M. Backes, “ML-leaks: Model and data independent membership inference attacks and defenses on machine learning models,” in
2018
Earlier work this paper cites.
S. Yeom, I. Giacomelli, M. Fredrikson, and S. Jha, “Privacy risk in machine learning: Analyzing the connection to overfitting,” in
2018
Earlier work this paper cites.
J. Zhang, Z. Gu, J. Jang, H. Wu, M. P. Stoecklin, H. Huang, and I. Molloy, “Protecting intellectual property of deep neural networks with watermarking,” in
2018
Earlier work this paper cites.
A. Ghorbani and J. Zou, “Data shapley: Equitable valuation of data for machine learning,” in
2019
Cited alongside, same era.
R. Jia, D. Dao, B. Wang, F. A. Hubis, N. M. Gurel, B. Li, C. Zhang, C. Spanos, and D. Song, “Efficient task-specific data valuation for nearest neighbor algorithms,”
2019
Cited alongside, same era.
R. Jia, D. Dao, B. Wang, F. A. Hubis, N. Hynes, N. M. Gürel, B. Li, C. Zhang, D. Song, and C. J. Spanos, “Towards efficient data valuation based on the shapley value,” in
2019
Cited alongside, same era.
M. Nasr, R. Shokri, and A. Houmansadr, “Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning,”
2019
Cited alongside, same era.
S. Szyller, B. G. Atli, S. Marchal, and N. Asokan, “DAWN: dynamic adversarial watermarking of neural networks,” in
2019
Z. Ying, Y. Zhang, and X. Liu, “Privacy-preserving in defending against membership inference attacks,” in
2020
Later among the works it cites.
S. Basu, P. Pope, and S. Feizi, “Influence functions in deep learning are fragile,” in
2021
Closest in time.
N. Carlini, S. Chien, M. Nasr, S. Song, A. Terzis, and F. Tramer, “Membership inference attacks from first principles,” in
2021
Closest in time.
V. Duddu, A. Boutet, and V. Shejwalkar, “Gecko: Reconciling privacy, accuracy and efficiency in embedded deep learning,” in
2021
Closest in time.
A. Hannun, C. Guo, and L. van der Maaten, “Measuring data leakage in machine-learning models with fisher information,” in
2021
Closest in time.
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Cited alongside, same era.
E. Tabassi, K. J. Burns, M. Hadjimichael, A. Molina-Markham, and J. Sexton, “A taxonomy and terminology of adversarial machine learning,” in
2019
Cited alongside, same era.
2019
Cited alongside, same era.
V. Feldman, “Does learning require memorization? a short tale about a long tail,” in
2020
Cited alongside, same era.
V. Feldman and C. Zhang, “What neural networks memorize and why: Discovering the long tail via influence estimation,” in
2020
Cited alongside, same era.
A. Ghorbani, M. Kim, and J. Zou, “A distributional framework for data valuation,” in
2020
Cited alongside, same era.
W. House, “Guidance for regulation of artificial intelligence applications,” in
2020
Cited alongside, same era.
K. Leino and M. Fredrikson, “Stolen memories: Leveraging model memorization for calibrated white-box membership inference,” in
2020
Cited alongside, same era.
2021
Closest in time.
R. Jia, F. Wu, X. Sun, J. Xu, D. Dao, B. Kailkhura, C. Zhang, B. Li, and D. Song, “Scalability vs. utility: Do we have to sacrifice one for the other in data importance quantification?” in
2021
Closest in time.
E. Kazim, D. M. T. Denny, and A. Koshiyama, “AI auditing and impact assessment: according to the uk information commissioner’s office,”
2021
Closest in time.
Z. Li and Y. Zhang, “Membership leakage in label-only exposures,” in
2021
Closest in time.
Y. Liu, R. Wen, X. He, A. Salem, Z. Zhang, M. Backes, E. D. Cristofaro, M. Fritz, and Y. Zhang, “Ml-doctor: Holistic risk assessment of inference attacks against machine learning models,” in
2021
Closest in time.
L. Song and P. Mittal, “Systematic evaluation of privacy risks of machine learning models,” in
2021
Closest in time.
2022
Closest in time.