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Machine Learning models face increased concerns regarding the storage of personal user data and adverse impacts of corrupted data like backdoors or systematic bias.
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Shokri, R., Stronati, M., Song, C., and Shmatikov, V · 2017
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Manipulating machine learning: Poisoning attacks and countermeasures for regression learning
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Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
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The secret sharer: Evaluating and testing unintended memorization in neural networks
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Making AI forget you: Data deletion in machine learning
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A discussion of ’adversarial examples are not bugs, they are features’: Adversarial examples are just bugs, too
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A survey on image data augmentation for deep learning
When machine unlearning jeopardizes privacy
Chen, M., Zhang, Z., Wang, T., Backes, M., Humbert, M., and Zhang, Y · 2021
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California company settles ftc allegations it deceived consumers about use of facial recognition in photo storage app, January 2021
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Adversarial examples make strong poisons
Fowl, L., Goldblum, M., Chiang, P.-y., Geiping, J., Czaja, W., and Goldstein, T · 2021
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Mixed-privacy forgetting in deep networks
Golatkar, A., Achille, A., Ravichandran, A., Polito, M., and Soatto, S · 2021
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Amnesiac machine learning
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Shorten, C. and Khoshgoftaar, T. M · 2019
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Neural cleanse: Identifying and mitigating backdoor attacks in neural networks
Wang, B., Yao, Y., Shan, S., Li, H., Viswanath, B., Zheng, H., and Zhao, B. Y · 2019
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Cutmix: Regularization strategy to train strong classifiers with localizable features
Yun, S., Han, D., Oh, S. J., Chun, S., Choe, J., and Yoo, Y · 2019
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Machine unlearning: Linear filtration for logit-based classifiers
Baumhauer, T., Schöttle, P., and Zeppelzauer, M · 2020
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What neural networks memorize and why: Discovering the long tail via influence estimation
Feldman, V. and Zhang, C · 2020
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Certified data removal from machine learning models
Guo, C., Goldstein, T., Hannun, A., and van der Maaten, L · 2020
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Instahide: Instance-hiding schemes for private distributed learning
Huang, Y., Song, Z., Li, K., and Arora, S · 2020
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Graves, L., Nagisetty, V., and Ganesh, V · 2021
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Deepobliviate: A powerful charm for erasing data residual memory in deep neural networks
He, Y., Meng, G., Chen, K., He, J., and Hu, X · 2021
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Moving beyond “algorithmic bias is a data problem”
Hooker, S · 2021
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Membership inference attacks on machine learning: A survey
Hu, H., Salcic, Z., Dobbie, G., and Zhang, X · 2021
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Approximate data deletion from machine learning models
Izzo, Z., Smart, M. A., Chaudhuri, K., and Zou, J · 2021
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Ssse: Efficiently erasing samples from trained machine learning models
Peste, A., Alistarh, D., and Lampert, C. H · 2021
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Large image datasets: A pyrrhic win for computer vision?
Prabhu, V. U. and Birhane, A · 2021
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Hedgecut: Maintaining randomised trees for low-latency machine unlearning
Schelter, S., Grafberger, S., and Dunning, T · 2021
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Learning with selective forgetting
Shibata, T., Irie, G., Ikami, D., and Mitsuzumi, Y · 2021
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Systematic evaluation of privacy risks of machine learning models
Song, L. and Mittal, P · 2021
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Machine unlearning of features and labels
Warnecke, A., Pirch, L., Wressnegger, C., and Rieck, K · 2021
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Chundawat, V. S., Tarun, A. K., Mandal, M., and Kankanhalli, M · 2022
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Fairness-aware pac learning from corrupted data
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Deep unlearning via randomized conditionally independent hessians
Mehta, R., Pal, S., Singh, V., and Ravi, S. N · 2022
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A law of adversarial risk, interpolation, and label noise
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How unfair is private learning?
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Arcane: An efficient architecture for exact machine unlearning
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Forget unlearning: Towards true data-deletion in machine learning
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