Time series data have numerous applications in big data analytics. However, they often cause privacy issues when collected from individuals. To address this problem, most existing works perturb the values in the time series while retaining their temporal order, which may lead to significant distortion of the values. Recently, we propose TLDP model that perturbs temporal perturbation to ensure privacy guarantee while retaining original values. It has shown great promise to achieve significantly higher utility than value perturbation mechanisms in many time series analysis. However, its practicability is still undermined by two factors, namely, utility cost of extra missing or empty values, and inflexibility of privacy budget settings. To address them, in this paper we propose {\it switch} as a new two-way operation for temporal perturbation, as opposed to the one-way {\it dispatch} operation. The former inherently eliminates the cost of missing, empty or repeated values. Optimizing switch operation in a {\it stateful} manner, we then propose $StaSwitch$ mechanism for time series release under TLDP. Through both analytical and empirical studies, we show that $StaSwitch$ has significantly higher utility for the published time series than any state-of-the-art temporal- or value-perturbation mechanism, while allowing any combination of privacy budget settings.
翻译:时间序列数据在大数据分析中有许多应用。 但是,当从个人收集时,它们往往会造成隐私问题。 为了解决这个问题,大多数现有工作在保留时间序列的同时干扰时间序列中的值值,而保留时间序列则可能导致价值的重大扭曲。 最近, 我们提议了TLDP模型, 干扰时间扰动, 以确保隐私保障, 并保留原始值。 在许多时间序列分析中, 它显示了实现大大高于价值扰动机制的效用的巨大希望。 但是, 它的可行性仍然受到两个因素的破坏, 即额外缺失或空值的效用成本, 以及隐私预算设置不灵活。 为了解决这些问题, 我们在本文件中提议 ~it开关作为时间扰动的新的双向操作, 而不是单向的 ~它发送。 前者本身就消除了缺失、 空的或重复的值的成本。 在许多时间序列分析中, 我们然后提议 $Stawitch 机制在 TLDP 下的时间序列下释放时间序列的 。 通过分析和实验性研究, 我们显示, 在任何时间序列中, 允许任何州- strachang 的汇率组合, 。