Machine Unlearning of Traffic State Estimation and Prediction

• URL: http://arxiv.org/abs/2507.17984v1
• Date: Wed, 23 Jul 2025 23:23:18 GMT
• Title: Machine Unlearning of Traffic State Estimation and Prediction
• Authors: Xin Wang, R. Tyrrell Rockafellar, Xuegang, Ban,
• Abstract summary: This study introduces a novel learning paradigm for TSEP-Machine Unlearning TSEP.<n>It enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data.<n>By empowering models to "unlearn," we aim to enhance the trustworthiness and reliability of data-driven traffic TSEP.
• Score: 4.442043151145212
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data-driven traffic state estimation and prediction (TSEP) relies heavily on data sources that contain sensitive information. While the abundance of data has fueled significant breakthroughs, particularly in machine learning-based methods, it also raises concerns regarding privacy, cybersecurity, and data freshness. These issues can erode public trust in intelligent transportation systems. Recently, regulations have introduced the "right to be forgotten", allowing users to request the removal of their private data from models. As machine learning models can remember old data, simply removing it from back-end databases is insufficient in such systems. To address these challenges, this study introduces a novel learning paradigm for TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data. By empowering models to "unlearn," we aim to enhance the trustworthiness and reliability of data-driven traffic TSEP.

Related papers

• Privacy Preservation through Practical Machine Unlearning [0.0]
  This paper examines methods such as Naive Retraining and Exact Unlearning via the SISA framework.<n>We explore the potential of integrating unlearning principles into Positive Unlabeled (PU) Learning to address challenges posed by partially labeled datasets.
  arXiv  Detail & Related papers  (2025-02-15T02:25:27Z)
• 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)
• Releasing Malevolence from Benevolence: The Menace of Benign Data on Machine Unlearning [28.35038726318893]
  Machine learning models trained on vast amounts of real or synthetic data often achieve outstanding predictive performance across various domains. To address privacy concerns, machine unlearning has been proposed to erase specific data samples from models. We introduce the Unlearning Usability Attack to distill data distribution information into a small set of benign data.
  arXiv  Detail & Related papers  (2024-07-06T15:42:28Z)
• Ungeneralizable Examples [70.76487163068109]
  Current approaches to creating unlearnable data involve incorporating small, specially designed noises. We extend the concept of unlearnable data to conditional data learnability and introduce textbfUntextbfGeneralizable textbfExamples (UGEs) UGEs exhibit learnability for authorized users while maintaining unlearnability for potential hackers.
  arXiv  Detail & Related papers  (2024-04-22T09:29:14Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Fast Machine Unlearning Without Retraining Through Selective Synaptic Dampening [51.34904967046097]
  Selective Synaptic Dampening (SSD) is a fast, performant, and does not require long-term storage of the training data. We present a novel two-step, post hoc, retrain-free approach to machine unlearning which is fast, performant, and does not require long-term storage of the training data.
  arXiv  Detail & Related papers  (2023-08-15T11:30:45Z)
• Certified Data Removal in Sum-Product Networks [78.27542864367821]
  Deleting the collected data is often insufficient to guarantee data privacy. UnlearnSPN is an algorithm that removes the influence of single data points from a trained sum-product network.
  arXiv  Detail & Related papers  (2022-10-04T08:22:37Z)
• A Survey of Machine Unlearning [56.017968863854186]
  Recent regulations now require that, on request, private information about a user must be removed from computer systems. ML models often remember' the old data. Recent works on machine unlearning have not been able to completely solve the problem.
  arXiv  Detail & Related papers  (2022-09-06T08:51:53Z)
• Machine unlearning via GAN [2.406359246841227]
  Machine learning models, especially deep models, may unintentionally remember information about their training data. We present a GAN-based algorithm to delete data in deep models, which significantly improves deleting speed compared to retraining from scratch.
  arXiv  Detail & Related papers  (2021-11-22T05:28:57Z)
• Privacy Enhancing Machine Learning via Removal of Unwanted Dependencies [21.97951347784442]
  This paper studies new variants of supervised and adversarial learning methods, which remove the sensitive information in the data before they are sent out for a particular application. The explored methods optimize privacy preserving feature mappings and predictive models simultaneously in an end-to-end fashion. Experimental results on mobile sensing and face datasets demonstrate that our models can successfully maintain the utility performances of predictive models while causing sensitive predictions to perform poorly.
  arXiv  Detail & Related papers  (2020-07-30T19:55:10Z)

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.