Authors

* External authors

Venue

Date

Share

Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization

Zijie Zhang*

Xin Zhao*

Tianshi Che*

Yang Zhou*

Lingjuan Lyu

* External authors

NeurIPS 2022

2022

Abstract

The right to be forgotten calls for efficient machine unlearning techniques that make trained machine learning models forget a cohort of data. The combination of training and unlearning operations in traditional machine unlearning methods often leads to the expensive computational cost on large-scale data. This paper presents a prompt certified machine unlearning algorithm, PCMU, which executes one-time operation of simultaneous training and unlearning in advance for a series of machine unlearning requests, without the knowledge of the removed/forgotten data. First, we establish a connection between randomized smoothing for certified robustness on classification and randomized smoothing for certified machine unlearning on gradient quantization. Second, we propose a prompt certified machine unlearning model based on randomized data smoothing and gradient quantization. We theoretically derive the certified radius R regarding the data change before and after data removals and the certified budget of data removals about R. Last but not least, we present another practical framework of randomized gradient smoothing and quantization, due to the dilemma of producing high confidence certificates in the first framework. We theoretically demonstrate the certified radius R' regarding the gradient change, the correlation between two types of certified radii, and the certified budget of data removals about R'.

Related Publications

Privacy Assessment on Reconstructed Images: Are Existing Evaluation Metrics Faithful to Human Perception?

NeurIPS, 2023
Xiaoxiao Sun*, Nidham Gazagnadou, Vivek Sharma, Lingjuan Lyu, Hongdong Li*, Liang Zheng*

Hand-crafted image quality metrics, such as PSNR and SSIM, are commonly used to evaluate model privacy risk under reconstruction attacks. Under these metrics, reconstructed images that are determined to resemble the original one generally indicate more privacy leakage. Image…

UltraRE: Enhancing RecEraser for Recommendation Unlearning via Error Decomposition

NeurIPS, 2023
Yuyuan Li*, Chaochao Chen*, Yizhao Zhang*, Weiming Liu*, Lingjuan Lyu, Xiaolin Zheng*, Dan Meng*, Jun Wang*

With growing concerns regarding privacy in machine learning models, regulations have committed to granting individuals the right to be forgotten while mandating companies to develop non-discriminatory machine learning systems, thereby fueling the study of the machine unlearn…

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

NeurIPS, 2023
Jiaqi Wang*, Xingyi Yang*, Suhan Cui*, Liwei Che*, Lingjuan Lyu, Dongkuan Xu*, Fenglong Ma*

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneo…

  • HOME
  • Publications
  • Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization

JOIN US

Shape the Future of AI with Sony AI

We want to hear from those of you who have a strong desire
to shape the future of AI.