Differentially Private Timeseries Forecasts for Networked Control

TL;DR

This paper proposes an incentive-based approach to improve differentially private timeseries forecasts used in control systems, reducing costs by optimizing noise levels in forecasts.

Contribution

It introduces a novel incentive allocation method for privacy-preserving forecasts, formulated as a biconvex optimization problem, enhancing control performance.

Findings

Reduced control costs by 2.5 times on synthetic data

Achieved 2.7 times cost reduction on Uber demand forecast

Demonstrated effectiveness of incentive-based noise reduction

Abstract

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.