TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning

• URL: http://arxiv.org/abs/2502.19770v2
• Date: Mon, 17 Mar 2025 23:51:45 GMT
• Title: TAPE: Tailored Posterior Difference for Auditing of Machine Unlearning
• Authors: Weiqi Wang, Zhiyi Tian, An Liu, Shui Yu,
• Abstract summary: We propose a TAilored Posterior diffErence (TAPE) method to provide unlearning auditing independently of original model training.<n>TAPE mimics unlearned posterior differences by quickly building unlearned shadow models.<n>We train a Reconstructor model to extract and evaluate the private information of the unlearned posterior differences to audit unlearning.
• Score: 19.99300962254467
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the increasing prevalence of Web-based platforms handling vast amounts of user data, machine unlearning has emerged as a crucial mechanism to uphold users' right to be forgotten, enabling individuals to request the removal of their specified data from trained models. However, the auditing of machine unlearning processes remains significantly underexplored. Although some existing methods offer unlearning auditing by leveraging backdoors, these backdoor-based approaches are inefficient and impractical, as they necessitate involvement in the initial model training process to embed the backdoors. In this paper, we propose a TAilored Posterior diffErence (TAPE) method to provide unlearning auditing independently of original model training. We observe that the process of machine unlearning inherently introduces changes in the model, which contains information related to the erased data. TAPE leverages unlearning model differences to assess how much information has been removed through the unlearning operation. Firstly, TAPE mimics the unlearned posterior differences by quickly building unlearned shadow models based on first-order influence estimation. Secondly, we train a Reconstructor model to extract and evaluate the private information of the unlearned posterior differences to audit unlearning. Existing privacy reconstructing methods based on posterior differences are only feasible for model updates of a single sample. To enable the reconstruction effective for multi-sample unlearning requests, we propose two strategies, unlearned data perturbation and unlearned influence-based division, to augment the posterior difference. Extensive experimental results indicate the significant superiority of TAPE over the state-of-the-art unlearning verification methods, at least 4.5$\times$ efficiency speedup and supporting the auditing for broader unlearning scenarios.

Related papers

• RESTOR: Knowledge Recovery through Machine Unlearning [71.75834077528305]
  Large language models trained on web-scale corpora can memorize undesirable datapoints.<n>Many machine unlearning algorithms have been proposed that aim to erase' these datapoints.<n>We propose the RESTOR framework for machine unlearning, which evaluates the ability of unlearning algorithms to perform targeted data erasure.
  arXiv  Detail & Related papers  (2024-10-31T20:54:35Z)
• Evaluating of Machine Unlearning: Robustness Verification Without Prior Modifications [15.257558809246524]
  Unlearning is a process enabling pre-trained models to remove the influence of specific training samples. Existing verification methods rely on machine learning attack techniques, such as membership inference attacks (MIAs) or backdoor attacks. We propose a novel verification scheme without any prior modifications, and can support verification on a much larger set.
  arXiv  Detail & Related papers  (2024-10-14T03:19:14Z)
• Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models [52.03511469562013]
  We introduce the Iterative Contrastive Unlearning (ICU) framework, which consists of three core components.<n>A Knowledge Unlearning Induction module targets specific knowledge for removal using an unlearning loss.<n>A Contrastive Learning Enhancement module preserves the model's expressive capabilities against the pure unlearning goal.<n>An Iterative Unlearning Refinement module dynamically adjusts the unlearning process through ongoing evaluation and updates.
  arXiv  Detail & Related papers  (2024-07-25T07:09:35Z)
• Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing [33.418062986773606]
  We first propose the framework of Machine Unlearning for Contrastive learning (MUC) and adapting existing methods. We observe that several methods are mediocre unlearners and existing auditing tools may not be sufficient for data owners to validate the unlearning effects in contrastive learning. We propose a novel method called Alignment (AC) by explicitly considering the properties of contrastive learning and optimizing towards novel metrics to easily verify unlearning.
  arXiv  Detail & Related papers  (2024-06-05T19:55:45Z)
• Distilled Datamodel with Reverse Gradient Matching [74.75248610868685]
  We introduce an efficient framework for assessing data impact, comprising offline training and online evaluation stages. Our proposed method achieves comparable model behavior evaluation while significantly speeding up the process compared to the direct retraining method.
  arXiv  Detail & Related papers  (2024-04-22T09:16:14Z)
• Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearning [16.809644622465086]
  We conduct the first investigation to understand the extent to which machine unlearning can leak the confidential content of unlearned data. Under the Machine Learning as a Service setting, we propose unlearning inversion attacks that can reveal the feature and label information of an unlearned sample. The experimental results indicate that the proposed attack can reveal the sensitive information of the unlearned data.
  arXiv  Detail & Related papers  (2024-04-04T06:37:46Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Zero-shot Retrieval: Augmenting Pre-trained Models with Search Engines [83.65380507372483]
  Large pre-trained models can dramatically reduce the amount of task-specific data required to solve a problem, but they often fail to capture domain-specific nuances out of the box. This paper shows how to leverage recent advances in NLP and multi-modal learning to augment a pre-trained model with search engine retrieval.
  arXiv  Detail & Related papers  (2023-11-29T05:33:28Z)
• Federated Unlearning via Active Forgetting [24.060724751342047]
  We propose a novel federated unlearning framework based on incremental learning. Our framework differs from existing federated unlearning methods that rely on approximate retraining or data influence estimation.
  arXiv  Detail & Related papers  (2023-07-07T03:07:26Z)
• Learning to Unlearn: Instance-wise Unlearning for Pre-trained Classifiers [71.70205894168039]
  We consider instance-wise unlearning, of which the goal is to delete information on a set of instances from a pre-trained model. We propose two methods that reduce forgetting on the remaining data: 1) utilizing adversarial examples to overcome forgetting at the representation-level and 2) leveraging weight importance metrics to pinpoint network parameters guilty of propagating unwanted information.
  arXiv  Detail & Related papers  (2023-01-27T07:53:50Z)
• On the Necessity of Auditable Algorithmic Definitions for Machine Unlearning [13.149070833843133]
  Machine unlearning, i.e. having a model forget about some of its training data, has become increasingly important as privacy legislation promotes variants of the right-to-be-forgotten. We first show that the definition that underlies approximate unlearning, which seeks to prove the approximately unlearned model is close to an exactly retrained model, is incorrect because one can obtain the same model using different datasets. We then turn to exact unlearning approaches and ask how to verify their claims of unlearning.
  arXiv  Detail & Related papers  (2021-10-22T16:16:56Z)
• Machine Unlearning of Features and Labels [72.81914952849334]
  We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
  arXiv  Detail & Related papers  (2021-08-26T04:42:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.