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Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks
Open accessAbstract
We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.
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“Second, most of the above schemes focus on relatively simple machine learning problems, such as linear logistic regression, or complex models with special assumptions [40, 41, 43, 51].”
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“Significantly, model manipulation methods usually give a verifiability guarantee using theory-based and information bound-based methods [40, 41, 43].”
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“The higher the degree of similarity, the better the effect of the unlearning process.”
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“Unlearning based on model shifting usually removes the impact of the unlearning dataset by calculating the influence on the model [40, 43].”
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“The information bound metric relies on KL-divergence to measure the information remaining about the unlearning dataset within the model after the unlearning process.”
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“For the data that need to be unlearned, the accuracy should ideally be the same as a model trained without seeing D u [40].”
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“Golatkar et al. [40, 43] devised a new metric for verifying the effectiveness of unlearning schemes, where they measured the upper bound of the residual information about samples that need to be unlearned.”
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“Golatkar et al. [40, 43, 52] verify the effectiveness of their unlearning schemes based on accuracy and relearning time.”
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“Then they evaluated the similarity of those distributions against the confidence of a trained model that has never seen the unlearning dataset.”
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Zhou, J., Li, H., Liao, X., Zhang, B., He, W., Li, Z., Zhou, L., & Gao, X. (2023) · Nature Communications · doi:10.1038/s41467-023-41703-x · Cited by 23
Kim, H., Lee, S. T., & Woo, S. S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i19.30118 · Cited by 21
An overview of machine unlearning
Li, C., Jiang, H., Chen, J., Zhao, Y. L., Fu, S., Jing, F., & Guo, Y. (2024) · High-Confidence Computing · doi:10.1016/j.hcc.2024.100254 · Cited by 21
Forget-SVGD: Particle-Based Bayesian Federated Unlearning
Gong, J., Kang, J., Simeone, O., & Kassab, R. (2022) · IEEE Data Science Workshop (DSW) · doi:10.1109/dslw53931.2022.9820602 · Cited by 21
Fast Model Update for IoT Traffic Anomaly Detection With Machine Unlearning
Fan, J., Wu, K., Zhou, Y., Zhao, Z., & Huang, S. (2022) · IEEE Internet of Things Journal · doi:10.1109/jiot.2022.3214840 · Cited by 21
Detect, Distill and Update: Learned DB Systems Facing Out of Distribution Data
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Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models
Poppi, S., Poppi, T., Cocchi, F., Cornia, M., Baraldi, L., & Cucchiara, R. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-73668-1_20 · Cited by 32
ERASER: Machine Unlearning in MLaaS via an Inference Serving-Aware Approach
Hu, Y., Lou, J., Liu, J., Ni, W., Lin, F., Qin, Z., & Ren, K. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670398 · Cited by 24
To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods
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Update Selective Parameters: Federated Machine Unlearning Based on Model Explanation
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Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience
Huynh, T. T., Nguyen, T. B., Nguyen, P. L., Nguyên, T. T., Weidlich, M., Nguyen, Q. V. H., & Aberer, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70362-1_4 · Cited by 19
Continual Forgetting for Pre-Trained Vision Models
Zhao, H., Ni, B., Fan, J., Wang, Y., Chen, Y., Meng, G., & Zhang, Z. (2024) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52733.2024.02705 · Cited by 24
Get Rid of Your Trail : Remotely Erasing Backdoors in Federated Learning
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Have You Forgotten? A Method to Assess if Machine Learning Models Have Forgotten Data
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DeltaBoost: Gradient Boosting Decision Trees with Efficient Machine Unlearning
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CPR: Retrieval Augmented Generation for Copyright Protection
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Self-citationMachine Unlearning for Image Retrieval: A Generative Scrubbing Approach
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Generative Unlearning for Any Identity
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NoT: Federated Unlearning via Weight Negation
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MUter: Machine Unlearning on Adversarially Trained Models
Liu, J., Xue, M., Lou, J., Zhang, X., Xiong, L., & Qin, Z. (2023) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00451 · Cited by 16
Learning to Unlearn for Robust Machine Unlearning
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A survey on machine unlearning: Techniques and new emerged privacy risks
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Verifiable and Provably Secure Machine Unlearning
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Federated UnlearningPreprint
Liu, G., Ma, X., Yang, Y., Wang, C., & Liu, J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.13891 · Cited by 33
Machine Unlearning in Gradient Boosting Decision Trees
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Machine Un-learning: An Overview of Techniques, Applications, and Future Directions
Sai, S., Mittal, U., Chamola, V., Huang, K., Spinelli, I., Scardapane, S., Tan, Z., & Hussain, A. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10219-3 · Cited by 18
Metadata
- Authors
- Golatkar, A., Achille, A., & Soatto, S.
- Affiliations
- University of California, Los Angeles (US)
- Venue
- 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2020-06
- Pages
- 9301-9309
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2985940692
- Semantic Scholar
- c956d133b78e0d9b20885593809f0b636ce34093
- Cited by
- 394
- References
- 32
- Citations by year
- 2026: 96 · 2025: 115 · 2024: 75 · 2023: 56 · 2022: 24 · 2021: 21 · 2020: 6
- FWCI
- 10.22
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Office of Naval Research (ONR N00014-17-1-2072, N00014-19-1-2229, ONR-N00014, N00014-17-1, N00014-19, N00014-19-1-2066, N00014-17-1-2072, N00014, N00014-17)
- Army Research Office (W911NF-17, W911NF-17-1-0304, W911NF, ARO W911NF-17-1-0304)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Keywords
- selective forgetting, deep neural network, differential privacy, information erasure, information leakage, privacy-preserving machine learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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