让交通预测模型可选择性遗忘敏感或过时数据，提升系统可信度。
Machine Unlearning of Traffic State Estimation and Prediction
- 提出机器遗忘框架，使模型能主动删除特定数据记忆
- 实现对隐私数据、污染数据或过期数据的精准清除
- 适合关注交通系统隐私与安全的研究者和工程师
基于数据驱动的交通状态估计与预测（TSEP）严重依赖包含敏感信息的数据源。尽管海量数据推动了机器学习方法的重大突破，但也带来了隐私泄露、网络安全和数据新鲜度等问题，削弱公众对智能交通系统的信任。近年来，“被遗忘权”法规要求用户可请求从模型中移除个人数据。然而，仅删除后端数据库中的数据不足以解决模型仍保留旧数据记忆的问题。为此，本研究提出一种新型TSEP机器遗忘范式——机器遗忘TSEP，使已训练的TSEP模型能够选择性遗忘隐私敏感、被污染或过时的数据。通过赋予模型“遗忘”能力，旨在增强数据驱动交通TSEP系统的可信度与可靠性。
原文摘要 · Abstract (English)
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TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data.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.
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