使用基于度量的隐私的时间序列清理

Liyue Fan, Luca Bonomi
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引用次数: 7

摘要

连接设备的日益普及已经产生了大量的时间序列数据。出于对消费者隐私的考虑,在与不受信任的第三方共享之前,必须对从个人设备收集的数据进行消毒。但是,现有的时间序列隐私解决方案不能为单个时间序列提供可证明的保证,并且可能无法扩展到来自广泛应用程序领域的数据。本文采用基于差分隐私的广义隐私概念对单个时间序列进行处理,并采用离散余弦变换对时间序列数据的特征进行建模。我们将先前报道的二维结果扩展到任意k维空间。对不同数据集的实证评估证明了我们提出的方法在标准均方误差(MSE)和分类任务中的适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Time Series Sanitization with Metric-Based Privacy
The increasing popularity of connected devices has given rise to the vast generation of time series data. Due to consumer privacy concerns, the data collected from individual devices must be sanitized before sharing with untrusted third-parties. However, existing time series privacy solutions do not provide provable guarantees for individual time series and may not extend to data from a wide range of application domains. In this paper, we adopt a generalized privacy notion based on differential privacy for individual time series sanitization and Discrete Cosine Transform to model the characteristics of time series data. We extend previously reported 2-dimensional results to arbitrary k-dimensional space. Empirical evaluation with various datasets demonstrates the applicability of our proposed method with the standard mean squared error (MSE) and in classification tasks.
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