Real-Time Trajectory Synthesis with Local Differential Privacy for Preserving Spatial-Temporal Patterns

Conceitos essenciais

The core message of this paper is to propose RetraSyn, a novel real-time trajectory synthesis framework that can perform on-the-fly trajectory synthesis based on the mobility patterns privately extracted from users' trajectory streams, enabling downstream trajectory analysis with privacy protection while preserving the spatial-temporal patterns.

Resumo

The paper proposes RetraSyn, a real-time trajectory synthesis framework that can protect users' privacy while preserving the spatial-temporal patterns in trajectory streams. The key components include:

Global Mobility Model: The curator constructs a global mobility model by aggregating users' perturbed transition states at each timestamp, which captures the continuous movement patterns.

Dynamic Mobility Update (DMU) Mechanism: DMU selectively updates the most informative parts of the mobility model on the fly, accurately capturing the changing patterns and reducing perturbation noise.

Real-time Synthesis: RetraSyn generates synthetic trajectories that align with the current updated spatial-temporal patterns using a Markov-based probabilistic model.

Adaptive Allocation Strategy: RetraSyn employs different portion-based allocation strategies to appropriately distribute the privacy budget or report users at each timestamp, considering the dynamics of real-world trajectory streams.

The proposed framework can perform real-time trajectory generation with local differential privacy, enabling downstream analysis on the high-utility synthesized data while protecting users' sensitive information. Extensive experiments on real-world and synthetic datasets demonstrate the superiority and versatility of RetraSyn.

Estatísticas

The paper reports the following key statistics:

T-Drive dataset has 232,640 trajectories with 3,167,316 points and average length of 13.61 over 886 timestamps.
Oldenburg dataset has 260,000 trajectories with 15,597,242 points and average length of 59.98 over 500 timestamps.
SanJoaquin dataset has 1,010,000 trajectories with 55,854,936 points and average length of 55.30 over 1,000 timestamps.

Citações

"RetraSyn constructs a global mobility model by aggregating users' perturbed transition states at each timestamp, which takes the continuous movement patterns into consideration."
"RetraSyn employs a synthesis-based framework to dynamically generate synthetic trajectories that align with the current learned spatial-temporal patterns."
"RetraSyn integrates entering and quitting events into our global mobility model to emulate the behaviors of genuine users."

Principais Insights Extraídos De

Real-Time Trajectory Synthesis with Local Differential Privacy

by Yujia Hu,Yun... às arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11450.pdf

Perguntas Mais Profundas

How can RetraSyn be extended to handle more complex spatial-temporal patterns, such as periodic or seasonal trends in trajectory data

To handle more complex spatial-temporal patterns like periodic or seasonal trends in trajectory data, RetraSyn can be extended by incorporating additional features and techniques. One approach could involve integrating time series analysis methods to identify and model recurring patterns in the trajectory data. This could include techniques like Fourier analysis to detect periodic trends or seasonal variations in the movement patterns of users. By capturing these patterns, RetraSyn can adjust its synthesis process to generate trajectories that reflect these temporal dynamics accurately. Additionally, incorporating machine learning algorithms such as recurrent neural networks (RNNs) or Long Short-Term Memory (LSTM) networks can help capture long-term dependencies and temporal correlations in the data, enabling the model to learn and replicate complex spatial-temporal patterns effectively.

What are the potential limitations of the Markov-based probabilistic model used for trajectory synthesis, and how could alternative generative models be explored to further improve the utility

The Markov-based probabilistic model used for trajectory synthesis in RetraSyn may have limitations in capturing intricate dependencies and non-linear relationships in the data. One potential limitation is the assumption of memorylessness inherent in Markov models, which may not fully capture the historical context and sequential dependencies present in trajectory data. To address this, alternative generative models like Hidden Markov Models (HMMs), Gaussian Processes, or Generative Adversarial Networks (GANs) could be explored. HMMs can model hidden states and transitions between them, allowing for more complex temporal dynamics to be captured. Gaussian Processes offer a non-parametric approach to modeling trajectories, enabling flexibility in capturing diverse patterns. GANs can generate realistic trajectories by learning the distribution of the data and generating samples that closely resemble the original trajectories. By exploring these alternative models, RetraSyn can potentially improve the fidelity and utility of the synthesized trajectories.

Given the focus on preserving spatial-temporal patterns, how could RetraSyn be adapted to also consider semantic or contextual information about the trajectories (e.g., purpose of travel, points of interest) to enhance the authenticity and usefulness of the synthesized data

To consider semantic or contextual information about trajectories in addition to spatial-temporal patterns, RetraSyn can be adapted by incorporating additional features and data sources. One approach is to integrate external contextual data sources such as points of interest, traffic conditions, weather information, or user demographics into the synthesis process. By incorporating these contextual factors, RetraSyn can generate trajectories that not only reflect movement patterns but also consider the purpose of travel, preferences, and behavior of users. This can enhance the authenticity and usefulness of the synthesized data for applications like personalized recommendations, urban planning, or targeted marketing. Furthermore, leveraging natural language processing techniques to analyze textual information associated with trajectories, such as user comments or check-in descriptions, can provide valuable insights into the semantic context of the trajectories. By combining spatial-temporal patterns with semantic information, RetraSyn can offer a more comprehensive and insightful representation of user mobility behavior.