Verifiable and Provably Secure Machine Unlearning

17 October 2022

Papers citing "Verifiable and Provably Secure Machine Unlearning"

21 / 21 papers shown

Title
A Framework for Cryptographic Verifiability of End-to-End AI Pipelines Kar Balan Robert Learney Tim Wood 34 0 0 28 Mar 2025
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models Haokun Chen Sebastian Szyller Weilin Xu N. Himayat MU AAML 43 0 0 20 Feb 2025
Verification of Machine Unlearning is Fragile Binchi Zhang Zihan Chen Cong Shen Jundong Li AAML 71 5 0 01 Aug 2024
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations Vasisht Duddu Oskari Jarvinen Lachlan J. Gunn Nirmal Asokan 69 1 0 25 Jun 2024
Federated Learning with Blockchain-Enhanced Machine Unlearning: A Trustworthy Approach Xuhan Zuo Minghao Wang Tianqing Zhu Lefeng Zhang Shui Yu Wanlei Zhou MU 29 4 0 27 May 2024
Unlearning during Learning: An Efficient Federated Machine Unlearning Method Hanlin Gu Gongxi Zhu Jie Zhang Xinyuan Zhao Yuxing Han Lixin Fan Qiang Yang MU 38 7 0 24 May 2024
zkLLM: Zero Knowledge Proofs for Large Language Models Haochen Sun Jason Li Hongyang Zhang ALM 34 22 0 24 Apr 2024
Attesting Distributional Properties of Training Data for Machine Learning Vasisht Duddu Anudeep Das Nora Khayata Hossein Yalame T. Schneider Nirmal Asokan 40 5 0 18 Aug 2023
Machine Unlearning: Solutions and Challenges Jie Xu Zihan Wu Cong Wang Xiaohua Jia MU 40 46 0 14 Aug 2023
zkDL: Efficient Zero-Knowledge Proofs of Deep Learning Training Hao-Lun Sun Tonghe Bai Jason Li Hongyang R. Zhang 34 19 0 30 Jul 2023
Ticketed Learning-Unlearning Schemes Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Ayush Sekhari Chiyuan Zhang MU 38 7 0 27 Jun 2023
Machine Unlearning: its nature, scope, and importance for a "delete culture" Luciano Floridi AILaw MU 32 6 0 24 May 2023
Exploring the Landscape of Machine Unlearning: A Comprehensive Survey and Taxonomy T. Shaik Xiaohui Tao Haoran Xie Lin Li Xiaofeng Zhu Qingyuan Li MU 36 25 0 10 May 2023
DeepReShape: Redesigning Neural Networks for Efficient Private Inference N. Jha Brandon Reagen 30 10 0 20 Apr 2023
Training Data Influence Analysis and Estimation: A Survey Zayd Hammoudeh Daniel Lowd TDI 29 82 0 09 Dec 2022
Knowledge Unlearning for Mitigating Privacy Risks in Language Models Joel Jang Dongkeun Yoon Sohee Yang Sungmin Cha Moontae Lee Lajanugen Logeswaran Minjoon Seo KELM PILM MU 147 191 0 04 Oct 2022
VeriFi: Towards Verifiable Federated Unlearning Xiangshan Gao Xingjun Ma Jingyi Wang Youcheng Sun Bo Li S. Ji Peng Cheng Jiming Chen MU 65 46 0 25 May 2022
Manipulating SGD with Data Ordering Attacks Ilia Shumailov Zakhar Shumaylov Dmitry Kazhdan Yiren Zhao Nicolas Papernot Murat A. Erdogdu Ross J. Anderson AAML 112 90 0 19 Apr 2021
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 253 93 0 11 Dec 2020
Machine Unlearning: Linear Filtration for Logit-based Classifiers Thomas Baumhauer Pascal Schöttle Matthias Zeppelzauer MU 107 130 0 07 Feb 2020
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware Florian Tramèr Dan Boneh FedML 114 395 0 08 Jun 2018