Min Du, Zhi Chen, Chang Liu, Rajvardhan Oak, and Dawn Song. Lifelong anomaly detection through unlearning. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, pages 1283-1297, 2019.
Cynthia Dwork, Frank McSherry, Kobbi Nissim, and Adam Smith. Calibrating noise to sensitivity in private data analysis. In Proceedings of the 3rd Conference on Theory of Cryptography, TCC '06, pages 265-284, Berlin, Heidelberg, 2006. Springer.
Federal Trade Commission. California company settles ftc allegations it deceived consumers about use of facial recognition in photo storage app, January 2021.
Jonas Geiping, Liam Fowl, W Ronny Huang, Wojciech Czaja, Gavin Taylor, Michael Moeller, and Tom Goldstein. Witches' brew: Industrial scale data poisoning via gradient matching. In Proceedings of the 9th International Conference on Learning Representations, ICLR '21, 2021.
General Data Protection Regulation. Regulation (EU) 2016/679 of the European parliament and of the council of 27 April 2016, 2016.
Antonio Ginart, Melody Guan, Gregory Valiant, and James Y Zou. Making AI forget you: Data deletion in machine learning. In Advances in Neural Information Processing Systems 32, NeurIPS '19, pages 3518-3531. Curran Associates, Inc., 2019.
Aditya Golatkar, Alessandro Achille, and Stefano Soatto. Eternal sunshine of the spotless net: Selective forgetting in deep networks. In Proceedings of the 2020 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR '20, pages 9304-9312, Washington, DC, USA, 2020. IEEE Computer Society.
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran, Marzia Polito, and Stefano Soatto. Mixed-privacy forgetting in deep networks. In Proceedings of the 2021 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR '21, pages 792-801. IEEE Computer Society, 2021.
Micah Goldblum, Dimitris Tsipras, Chulin Xie, Xinyun Chen, Avi Schwarzschild, Dawn Song, Aleksander Madry, Bo Li, and Tom Goldstein. Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses. arXiv preprint arXiv:2012.10544, 2020.
Laura Graves, Vineel Nagisetty, and Vijay Ganesh. Amnesiac machine learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 11516-11524, 2021.
Tianyu Gu, Brendan Dolan-Gavitt, and Siddharth Garg. Badnets: Identifying vulnerabilities in the machine learning model supply chain. arXiv preprint arXiv:1708.06733, 2017.
Chuan Guo, Tom Goldstein, Awni Hannun, and Laurens Van Der Maaten. Certified data removal from machine learning models. In Proceedings of the 37th International Conference on Machine Learning, ICML '20, pages 3832-3842. JMLR, Inc., 2020.
Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. Adaptive machine unlearning. In Advances in Neural Information Processing Systems 34, NeurIPS '21, pages 16319-16330. Curran Associates, Inc., 2021.
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the 2016 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR '16, pages 770-778, Washington, DC, USA, 2016. IEEE Computer Society.
Jeremy Howard. Imagenette, 2019. URL https://github.com/fastai/imagenette/.
W Ronny Huang, Jonas Geiping, Liam Fowl, Gavin Taylor, and Tom Goldstein. Metapoison: Practical general-purpose clean-label data poisoning. In Advances in Neural Information Processing Systems 33, NeurIPS '20, pages 12080-12091. Curran Associates, Inc., 2020.
Information Commissioner's Office. Guidance on the ai auditing framework, February 2020.