论文标题
翻转：保留时间序列的隐私机制
FLIP: A Utility Preserving Privacy Mechanism for Time Series
论文作者
论文摘要
确保已发布数据中的隐私是数据生产机构的重要目标。近年来，已经进行了有关开发合适的隐私机制的广泛研究。特别值得注意的是增加噪音的想法，并保证了差异隐私。但是，当应用非常严格的隐私机制时，会担心损害数据实用程序。这样的妥协在相关数据（例如时间序列数据）中可能非常明显。将白噪声添加到随机过程中可能会显着改变相关结构，这对于最佳预测至关重要。我们建议使用全通过滤作为定期采样时间序列数据的隐私机制，表明此过程保留了实用性，同时还为实体级别的时间序列提供了足够的隐私保证。
Guaranteeing privacy in released data is an important goal for data-producing agencies. There has been extensive research on developing suitable privacy mechanisms in recent years. Particularly notable is the idea of noise addition with the guarantee of differential privacy. There are, however, concerns about compromising data utility when very stringent privacy mechanisms are applied. Such compromises can be quite stark in correlated data, such as time series data. Adding white noise to a stochastic process may significantly change the correlation structure, a facet of the process that is essential to optimal prediction. We propose the use of all-pass filtering as a privacy mechanism for regularly sampled time series data, showing that this procedure preserves utility while also providing sufficient privacy guarantees to entity-level time series.
