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Arxiv
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What is the Cost of Differential Privacy for Deep Learning-Based Trajectory Generation?
- Differential Privacy (DP) is used to protect sensitive personal information in location trajectories but balancing utility and privacy is difficult.
- Deep learning-based generative models are used to create synthetic trajectories, lacking formal privacy guarantees and relying on conditional information.
- A study evaluated the utility cost of enforcing DP in these models across two datasets and eleven utility metrics.
- The evaluation looked at the impact of DP-SGD on generative models and proposed a novel DP mechanism for conditional generation with formal guarantees.
- Diffusion, VAE, and GAN model types were analyzed for their effects on the utility-privacy trade-off.
- Results indicated that DP-SGD significantly affects performance, with some utility remaining for large datasets.
- The proposed DP mechanism enhances training stability, especially for GANs and smaller datasets.
- Diffusion models show the best utility without guarantees, but GANs perform best with DP-SGD.
- It suggests that the optimal non-private model may not be the best choice when considering formal guarantees.
- DP trajectory generation remains challenging and formal guarantees are currently more feasible with large datasets and in specific use cases.
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