Balancing Privacy and Utility of Spatio-Temporal Data for Taxi-Demand Prediction
Author:
Affiliation:
1. Osaka University,Suita,Japan
2. Tanta University,Tanta,Egypt
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10214688/10214888/10214915.pdf?arnumber=10214915
Reference23 articles.
1. Privacy-preserving blockchain-based federated learning for traffic flow prediction
2. Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence
3. LocMIA: Membership Inference Attacks Against Aggregated Location Data
4. Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
5. Exploring the effectiveness of geomasking techniques for protecting the geoprivacy of Twitter users
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1. HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization;Proceedings of the VLDB Endowment;2024-07
2. Evaluating the utility of human mobility data under local differential privacy;2024 25th IEEE International Conference on Mobile Data Management (MDM);2024-06-24
3. Towards Anonymizing Intermodal Mobility Data for Smart Cities;Proceedings of the 1st ACM SIGSPATIAL International Workshop on Geo-Privacy and Data Utility for Smart Societies;2023-11-13
4. DEMO: STM - A Privacy-Enhanced Solution for Spatio-Temporal Trajectory Management;2023 24th IEEE International Conference on Mobile Data Management (MDM);2023-07
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