论文标题
差异性私人时间表预测网络控制
Differentially Private Timeseries Forecasts for Networked Control
论文作者
论文摘要
我们分析了一个成本最小化的问题，其中控制器依赖于不完善的时间表预测。预测模型会产生不完美的预测，因为它们使用匿名噪声来保护输入数据隐私。但是，这种噪声增加了控制成本。我们考虑了一种场景，控制器支付预测模型激励措施以减少噪声，并将预测结合在一起。然后，控制器使用预测做出控制决策。因此，预测模型在接受激励措施和保护隐私之间面临权衡。我们提出了一种分配经济激励措施并最大程度地减少成本的方法。我们在线性二次调节器上解决了比科结构优化问题，并将我们的方法与统一的激励分配方案进行了比较。最终的解决方案分别为合成时间和Uber需求预测降低了控制成本的2.5倍和2.7倍。
We analyze a cost-minimization problem in which the controller relies on an imperfect timeseries forecast. Forecasting models generate imperfect forecasts because they use anonymization noise to protect input data privacy. However, this noise increases the control cost. We consider a scenario where the controller pays forecasting models incentives to reduce the noise and combines the forecasts into one. The controller then uses the forecast to make control decisions. Thus, forecasting models face a trade-off between accepting incentives and protecting privacy. We propose an approach to allocate economic incentives and minimize costs. We solve a biconvex optimization problem on linear quadratic regulators and compare our approach to a uniform incentive allocation scheme. The resulting solution reduces control costs by 2.5 and 2.7 times for the synthetic timeseries and the Uber demand forecast, respectively.
