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ref.bib
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@inproceedings{wu2020DeltaGradRapidRetraining,
title = {{DeltaGrad}: {Rapid} retraining of machine learning models},
volume = {PartF16814},
copyright = {3},
shorttitle = {{DeltaGrad}},
url = {http://arxiv.org/abs/2006.14755},
abstract = {Machine learning models are not static and may need to be retrained on slightly changed datasets, for instance, with the addition or deletion of a set of data points. This has many applications, including privacy, robustness, bias reduction, and uncertainty quantifcation. However, it is expensive to retrain models from scratch. To address this problem, we propose the DeltaGrad algorithm for rapid retraining machine learning models based on information cached during the training phase. We provide both theoretical and empirical support for the effectiveness of DeltaGrad, and show that it compares favorably to the state of the art.},
language = {en},
urldate = {2023-04-29},
booktitle = {37th {International} {Conference} on {Machine} {Learning} ({ICML})},
publisher = {arXiv},
author = {Wu, Yinjun and Dobriban, Edgar and Davidson, Susan B.},
month = jun,
year = {2020},
note = {arXiv:2006.14755 [cs, stat]
CCF: A},
keywords = {⛔ No INSPIRE recid found, ⛔ No DOI found, Computer Science - Machine Learning, Statistics - Machine Learning},
pages = {10286--10297},
file = {arXiv.org Snapshot:/Users/zihanwu/Zotero/storage/NBY2RCQE/2006.html:text/html;Wu et al_2020_DeltaGrad.pdf:/Users/zihanwu/Zotero/storage/Q4M4SVEL/Wu et al_2020_DeltaGrad.pdf:application/pdf},
}
@inproceedings{sekhari2021RememberWhatYou,
title = {Remember {What} {You} {Want} to {Forget}: {Algorithms} for {Machine} {Unlearning}},
volume = {34},
copyright = {2},
shorttitle = {Remember {What} {You} {Want} to {Forget}},
url = {https://proceedings.neurips.cc/paper/2021/hash/9627c45df543c816a3ddf2d8ea686a99-Abstract.html},
language = {en},
urldate = {2023-04-16},
booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
publisher = {Curran Associates, Inc.},
author = {Sekhari, Ayush and Acharya, Jayadev and Kamath, Gautam and Suresh, Ananda Theertha},
year = {2021},
note = {titleTranslation: 记住你想忘记的东西：机器解除学习的算法
CCF: A},
keywords = {⛔ No INSPIRE recid found, /unread, ⛔ No DOI found, \_tablet},
pages = {18075--18086},
file = {Remember_What_You_Want_to_Forget_Sekhari_et_al_2021.pdf:/Users/zihanwu/Zotero/storage/8FDGVXS3/Remember_What_You_Want_to_Forget_Sekhari_et_al_2021.pdf:application/pdf},
}
@inproceedings{guo2020CertifiedDataRemovala,
title = {Certified data removal from machine learning models},
volume = {PartF16814},
copyright = {1},
booktitle = {37th {International} {Conference} on {Machine} {Learning} ({ICML})},
author = {Guo, Chuan and Goldstein, Tom and Hannun, Awni and Van Der Maaten, Laurens},
year = {2020},
note = {Issue: i
CCF: A},
keywords = {⛔ No DOI found},
pages = {3790--3800},
file = {Guo 等 - 2019 - Certified data removal from machine learning model.3525297:/Users/zihanwu/Zotero/storage/5L252ZJW/Guo 等 - 2019 - Certified data removal from machine learning model.3525297:application/pdf},
}
@inproceedings{shibata2021LearningSelectiveForgetting,
title = {Learning with {Selective} {Forgetting}},
volume = {2},
copyright = {8},
doi = {10.24963/ijcai.2021/137},
booktitle = {{IJCAI} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
author = {Shibata, Takumi and Irie, Genta and Ikami, Daichi and Mitsuzumi, Yoshiki},
year = {2021},
note = {Issue: 4
CCF: A},
keywords = {⛔ No DOI found},
pages = {989--996},
file = {Shibata et al_2021_Learning with Selective Forgetting.pdf:/Users/zihanwu/Zotero/storage/E62H7KPM/Shibata et al_2021_Learning with Selective Forgetting.pdf:application/pdf},
}
@inproceedings{golatkar2020EternalSunshineSpotlessa,
title = {Eternal sunshine of the spotless net: {Selective} forgetting in deep networks},
copyright = {4},
shorttitle = {Eternal sunshine of the spotless net},
doi = {10.1109/CVPR42600.2020.00932},
booktitle = {Proceedings of the {IEEE}/{CVF} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
author = {Golatkar, Aditya and Achille, Alessandro and Soatto, Stefano},
year = {2020},
note = {CCF: A},
keywords = {⛔ No DOI found},
pages = {9304--9312},
file = {Golatkar et al_2020_Eternal sunshine of the spotless net.pdf:/Users/zihanwu/Zotero/storage/RJ7GMK4Z/Golatkar et al_2020_Eternal sunshine of the spotless net.pdf:application/pdf;Golatkar_Eternal_Sunshine_of_CVPR_2020_supplemental.pdf:/Users/zihanwu/Zotero/storage/MKK7DRP3/Golatkar_Eternal_Sunshine_of_CVPR_2020_supplemental.pdf:application/pdf;Snapshot:/Users/zihanwu/Zotero/storage/EE2RQI6C/Golatkar_Eternal_Sunshine_of_the_Spotless_Net_Selective_Forgetting_in_Deep_CVPR_2020_paper.html:text/html},
}
@inproceedings{golatkar2020ForgettingOutsideBoxa,
title = {Forgetting outside the box: {Scrubbing} deep networks of information accessible from input-output observations},
copyright = {7},
shorttitle = {Forgetting outside the box},
booktitle = {Computer {Vision}–{ECCV} 2020: 16th {European} {Conference}, {Glasgow}, {UK}, {August} 23–28, 2020, {Proceedings}, {Part} {XXIX} 16},
publisher = {Springer},
author = {Golatkar, Aditya and Achille, Alessandro and Soatto, Stefano},
year = {2020},
note = {CCF: B},
keywords = {/unread, ⛔ No DOI found},
pages = {383--398},
file = {Golatkar et al_2020_Forgetting outside the box.pdf:/Users/zihanwu/Zotero/storage/4VMRLCRD/Golatkar et al_2020_Forgetting outside the box.pdf:application/pdf;Snapshot:/Users/zihanwu/Zotero/storage/K8PJZR2I/978-3-030-58526-6_23.html:text/html},
}
@inproceedings{golatkar_mixed-privacy_2021,
title = {Mixed-{Privacy} {Forgetting} in {Deep} {Networks}},
copyright = {5},
url = {https://openaccess.thecvf.com/content/CVPR2021/html/Golatkar_Mixed-Privacy_Forgetting_in_Deep_Networks_CVPR_2021_paper.html},
language = {en},
urldate = {2023-06-16},
author = {Golatkar, Aditya and Achille, Alessandro and Ravichandran, Avinash and Polito, Marzia and Soatto, Stefano},
year = {2021},
note = {CCF: A},
keywords = {/unread},
pages = {792--801},
file = {Golatkar et al_2021_Mixed-Privacy Forgetting in Deep Networks.pdf:/Users/zihanwu/Zotero/storage/8XNCIDC4/Golatkar et al_2021_Mixed-Privacy Forgetting in Deep Networks.pdf:application/pdf},
}
@inproceedings{suriyakumar_algorithms_2022,
title = {Algorithms that {Approximate} {Data} {Removal}: {New} {Results} and {Limitations}},
volume = {35},
copyright = {6},
shorttitle = {Algorithms that {Approximate} {Data} {Removal}},
booktitle = {Advances in {Neural} {Information} {Processing} {Systems}},
author = {Suriyakumar, Vinith and Wilson, Ashia C.},
year = {2022},
note = {CCF: A},
keywords = {/unread, ⛔ No DOI found},
pages = {18892--18903},
file = {Snapshot:/Users/zihanwu/Zotero/storage/ZN6RBSNU/77c7faab15002432ba1151e8d5cc389a-Abstract-Conference.html:text/html;Suriyakumar_Wilson_2022_Algorithms that Approximate Data Removal.pdf:/Users/zihanwu/Zotero/storage/GFLU959J/Suriyakumar_Wilson_2022_Algorithms that Approximate Data Removal.pdf:application/pdf},
}
@inproceedings{mehta_deep_2022,
title = {Deep {Unlearning} via {Randomized} {Conditionally} {Independent} {Hessians}},
copyright = {10},
url = {https://openaccess.thecvf.com/content/CVPR2022/html/Mehta_Deep_Unlearning_via_Randomized_Conditionally_Independent_Hessians_CVPR_2022_paper.html},
language = {en},
urldate = {2023-06-16},
author = {Mehta, Ronak and Pal, Sourav and Singh, Vikas and Ravi, Sathya N.},
year = {2022},
note = {CCF: A},
keywords = {/unread},
pages = {10422--10431},
file = {Mehta et al_2022_Deep Unlearning via Randomized Conditionally Independent Hessians.pdf:/Users/zihanwu/Zotero/storage/96CEEGGB/Mehta et al_2022_Deep Unlearning via Randomized Conditionally Independent Hessians.pdf:application/pdf},
}
@inproceedings{graves_amnesiac_2021,
title = {Amnesiac {Machine} {Learning}},
volume = {35},
copyright = {9},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/17371},
doi = {10.1609/aaai.v35i13.17371},
abstract = {The Right to be Forgotten is part of the recently enacted General Data Protection Regulation (GDPR) law that affects any data holder that has data on European Union residents. It gives EU residents the ability to request deletion of their personal data, including training records used to train machine learning models. Unfortunately, Deep Neural Network models are vulnerable to information leaking attacks such as model inversion attacks which extract class information from a trained model and membership inference attacks which determine the presence of an example in a model's training data. If a malicious party can mount an attack and learn private information that was meant to be removed, then it implies that the model owner has not properly protected their user's rights and their models may not be compliant with the GDPR law. In this paper, we present two efficient methods that address this question of how a model owner or data holder may delete personal data from models in such a way that they may not be vulnerable to model inversion and membership inference attacks while maintaining model efficacy. We start by presenting a real-world threat model that shows that simply removing training data is insufficient to protect users. We follow that up with two data removal methods, namely Unlearning and Amnesiac Unlearning, that enable model owners to protect themselves against such attacks while being compliant with regulations. We provide extensive empirical analysis that show that these methods are indeed efficient, safe to apply, effectively remove learned information about sensitive data from trained models while maintaining model efficacy.},
language = {en},
urldate = {2023-06-16},
booktitle = {Proceedings of the {AAAI} {Conference} on {Artificial} {Intelligence}},
author = {Graves, Laura and Nagisetty, Vineel and Ganesh, Vijay},
month = may,
year = {2021},
note = {Number: 13
CCF: A},
keywords = {/unread, Security},
pages = {11516--11524},
file = {Graves et al_2021_Amnesiac Machine Learning.pdf:/Users/zihanwu/Zotero/storage/3MAIF4Q6/Graves et al_2021_Amnesiac Machine Learning.pdf:application/pdf},
}