Machine Unlearning of Traffic State Estimation and Prediction
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TSEP-Machine Unlearning TSEP-which enables a trained TSEP model to selectively forget privacy-sensitive, poisoned, or outdated data.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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Machine Unlearning for Causal Inference
Machine learning models play a vital role in making predictions and deriving insights from data and are being increasingly used for causal inference. To preserve user privacy, it is important to enable the model to forge…
Causal InferenceMachine UnlearningTowards Machine Unlearning Benchmarks: Forgetting the Personal Identities in Facial Recognition Systems
Machine unlearning is a crucial tool for enabling a classification model to forget specific data that are used in the training time. Recently, various studies have presented machine unlearning algorithms and evaluated th…
Age EstimationAttributeClassificationFacial Attribute Classification+2EVE: Efficient Verification of Data Erasure through Customized Perturbation in Approximate Unlearning
Verifying whether the machine unlearning process has been properly executed is critical but remains underexplored. Some existing approaches propose unlearning verification methods based on backdooring techniques. However…
Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach
Machine unlearning seeks to remove the influence of specified data from a trained model. While metrics such as unlearning accuracy (UA) and membership inference attack (MIA) provide baselines for assessing unlearning per…
Adversarial AttackConformal Predictionimage-classificationImage Classification+5Inference-time Unlearning Using Conformal Prediction
Machine unlearning is the process of efficiently removing specific information from a trained machine learning model without retraining from scratch. Existing unlearning methods, which often provide provable guarantees, …