基于平均场朗之万动力学的私有连续时间轨迹生成方法
Private Continuous-Time Synthetic Trajectory Generation via Mean-Field Langevin Dynamics
- 利用平均场朗之万动力学实现连续时间数据私有生成
- 单时间点贡献即可保障隐私，优于需完整轨迹的方法
- 适用于医疗等敏感时序数据场景，兼顾隐私与数据效用
本文提出一种算法，用于在高度敏感的时序数据领域（如医疗）中私有生成连续时间数据（例如随机微分方程的边缘分布）。该方法基于轨迹推断与连续时间合成数据生成之间的联系，并采用基于平均场朗之万动力学的计算方法。由于离散化的平均场朗之万动力学与带噪声的粒子梯度下降等价，可直接应用噪声SGD的差分隐私结果。实验在手绘MNIST数据的变体上生成了逼真轨迹，同时保持有意义的隐私保证。关键优势在于，本方法只需每人贡献单一时间点数据，而以往方法要求每人提供完整时间轨迹——从构造上显著提升隐私性。
原文摘要 · Abstract (English)
We provide an algorithm to privately generate continuous-time data (e.g. marginals from stochastic differential equations), which has applications in highly sensitive domains involving time-series data such as healthcare. We leverage the connections between trajectory inference and continuous-time synthetic data generation, along with a computational method based on mean-field Langevin dynamics. As discretized mean-field Langevin dynamics and noisy particle gradient descent are equivalent, DP results for noisy SGD can be applied to our setting. We provide experiments that generate realistic trajectories on a synthesized variation of hand-drawn MNIST data while maintaining meaningful privacy guarantees. Crucially, our method has strong utility guarantees under the setting where each person contributes data for \emph{only one time point}, while prior methods require each person to contribute their \emph{entire temporal trajectory}--directly improving the privacy characteristics by construction.
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