Differentially Private Spatiotemporal Trajectory Synthesis with Retained Data Utility

• URL: http://arxiv.org/abs/2408.12842v1
• Date: Fri, 23 Aug 2024 05:17:36 GMT
• Title: Differentially Private Spatiotemporal Trajectory Synthesis with Retained Data Utility
• Authors: Yuqing Ge, Yunsheng Wang, Nana Wang,
• Abstract summary: DP-STTS is a differentially private synthesizer with high data utility. Synthetic trajectories are generated from the noisy model. Experiments one real-life dataset demonstrate that DP-STTS provides good data utility.
• Score: 0.3277163122167433
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Spatiotemporal trajectories collected from GPS-enabled devices are of vital importance to many applications, such as urban planning and traffic analysis. Due to the privacy leakage concerns, many privacy-preserving trajectory publishing methods have been proposed. However, most of them could not strike a good balance between privacy protection and good data utility. In this paper, we propose DP-STTS, a differentially private spatiotemporal trajectory synthesizer with high data utility, which employs a model composed of a start spatiotemporal cube distribution and a 1-order Markov process. Specially, DP-STTS firstly discretizes the raw spatiotemporal trajectories into neighboring cubes, such that the model size is limited and the model's tolerance for noise could be enhanced. Then, a Markov process is utilized for the next location point picking. After adding noise under differential privacy (DP) to the model, synthetic trajectories that preserve essential spatial and temporal characteristics of the real trajectories are generated from the noisy model. Experiments on one real-life dataset demonstrate that DP-STTS provides good data utility. Our code is available at https://github.com/Etherious72/DP-STTS.

Related papers

• Reconsidering utility: unveiling the limitations of synthetic mobility data generation algorithms in real-life scenarios [49.1574468325115]
  We evaluate the utility of five state-of-the-art synthesis approaches in terms of real-world applicability. We focus on so-called trip data that encode fine granular urban movements such as GPS-tracked taxi rides. One model fails to produce data within reasonable time and another generates too many jumps to meet the requirements for map matching.
  arXiv  Detail & Related papers  (2024-07-03T16:08:05Z)
• PeFAD: A Parameter-Efficient Federated Framework for Time Series Anomaly Detection [51.20479454379662]
  We propose a. Federated Anomaly Detection framework named PeFAD with the increasing privacy concerns. We conduct extensive evaluations on four real datasets, where PeFAD outperforms existing state-of-the-art baselines by up to 28.74%.
  arXiv  Detail & Related papers  (2024-06-04T13:51:08Z)
• ST-DPGAN: A Privacy-preserving Framework for Spatiotemporal Data Generation [19.18074489351738]
  We propose a Graph-based model for generating privacy-protected data. Experiments conducted on three real-worldtemporal datasets validate the efficacy of our model. The prediction model trained on our generated data maintains a competitive edge compared to the model trained on the original data.
  arXiv  Detail & Related papers  (2024-06-04T04:43:54Z)
• DPTraj-PM: Differentially Private Trajectory Synthesis Using Prefix Tree and Markov Process [1.800676987432211]
  We propose DPTraj-PM, a method to synthesize trajectory dataset under the differential privacy framework. DPTraj-PM discretizes the raw trajectories into neighboring cells, and models them by combining a prefix tree structure and an m-order Markov process. The output traces crafted by DPTraj-PM not only preserves the patterns and variability in individuals' mobility behaviors, but also protects individual privacy.
  arXiv  Detail & Related papers  (2024-04-22T11:52:11Z)
• Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing Values [67.76168547245237]
  We introduce a novel framework called GST-Pro, which utilizes a graphtemporal process and anomaly scorer to detect anomalies. Our experimental results show that the GST-Pro method can effectively detect anomalies in time series data and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-01-11T10:10:16Z)
• Layout Sequence Prediction From Noisy Mobile Modality [53.49649231056857]
  Trajectory prediction plays a vital role in understanding pedestrian movement for applications such as autonomous driving and robotics. Current trajectory prediction models depend on long, complete, and accurately observed sequences from visual modalities. We propose LTrajDiff, a novel approach that treats objects obstructed or out of sight as equally important as those with fully visible trajectories.
  arXiv  Detail & Related papers  (2023-10-09T20:32:49Z)
• DiffTraj: Generating GPS Trajectory with Diffusion Probabilistic Model [44.490978394267195]
  We propose a spatial-temporal probabilistic model for trajectory generation (DiffTraj) The core idea is to reconstruct and synthesize geographic trajectories from white noise through a reverse trajectory denoising process. Experiments on two real-world datasets show that DiffTraj can be intuitively applied to generate high-fidelity trajectories.
  arXiv  Detail & Related papers  (2023-04-23T08:42:45Z)
• Automated classification of pre-defined movement patterns: A comparison between GNSS and UWB technology [55.41644538483948]
  Real-time location systems (RTLS) allow for collecting data from human movement patterns. The current study aims to design and evaluate an automated framework to classify human movement patterns in small areas.
  arXiv  Detail & Related papers  (2023-03-10T14:46:42Z)
• Don't Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence [73.14373832423156]
  We propose DP-Sinkhorn, a novel optimal transport-based generative method for learning data distributions from private data with differential privacy. Unlike existing approaches for training differentially private generative models, we do not rely on adversarial objectives.
  arXiv  Detail & Related papers  (2021-11-01T18:10:21Z)
• LSTM-TrajGAN: A Deep Learning Approach to Trajectory Privacy Protection [2.1793134762413437]
  We propose an end-to-end deep learning model to generate privacy-preserving synthetic trajectory data for data sharing and publication. The model is evaluated on the trajectory-user-linking task on a real-world semantic trajectory dataset.
  arXiv  Detail & Related papers  (2020-06-14T03:04:19Z)

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.