Spectral Differential Privacy: Application to Smart Meter Data
Kendall Parker, Matthew Hale, Prabir Barooah · IEEE Internet of Things Journal · 2021
We present spectral differential privacy (SpDP), a novel form of differential privacy (DP) designed to protect the frequency content of time-series data that come from wide sense stationary (WSS) stochastic processes. This notion is motivated by privacy needs in applications with time-series data over unbounded time, such as smart meters. First, a notion of DP on the space of (discretized) spectral densities is introduced. A Gaussian-like mechanism for SpDP is then presented that provides DP to the spectral density. Next, a novel streaming implementation is developed to enable real-time use of the proposed mechanism. The privacy guarantee provided by SpDP is independent of the time duration over which data are collected or shared. In contrast, time-domain trajectory-level DP (TrDP) will require noise with large variance to provide privacy over an extended time duration. The technique is numerically evaluated using smart meter data from a single home to compare the utility of SpDP to that of time-domain TrDP. The noise added by SpDP is substantially smaller than that added by time-domain TrDP, particularly when privacy over long time horizons is sought by TrDP.