Spectral Differential Privacy: Application to Smart Meter Data
Spectral Differential Privacy: Application to Smart Meter Data
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DOI：
10.1109/jiot.2021.3107770
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发表时间：
2021-02
影响因子：
10.6
通讯作者：
Kendall Parker;M. Hale;P. Barooah
中科院分区：
文献类型：
--
作者：
Kendall Parker;M. Hale;P. Barooah
我们提出了频谱差分隐私（SpDP），这是一种新颖的差分隐私（DP）形式，旨在保护来自广义平稳（WSS）随机过程的时间序列数据的频率内容。这一概念是由具有无限时间的时间序列数据的应用程序（例如智能电表）的隐私需求所激发的。首先，引入（离散）谱密度空间上的 DP 概念。然后提出了一种类似高斯的 SpDP 机制，为谱密度提供 DP。接下来，开发了一种新颖的流实现，以实现所提出的机制的实时使用。 SpDP 提供的隐私保证与收集或共享数据的持续时间无关。相比之下，时域轨迹级 DP (TrDP) 将需要具有大方差的噪声才能在较长时间内提供隐私。使用来自单个家庭的智能电表数据对该技术进行数值评估，以比较 SpDP 与时域 TrDP 的效用。 SpDP 添加的噪声远小于时域 TrDP 添加的噪声，特别是当 TrDP 寻求长时间范围内的隐私时。
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.