Differential Privacy (DP) has received increasing attention as a rigorous privacy framework. Many existing studies employ traditional DP mechanisms (e.g., the Laplace mechanism) as primitives to continuously release private data for protecting privacy at each time point (i.e., event-level privacy), which assume that the data at different time points are independent, or that adversaries do not have knowledge of correlation between data. However, continuously generated data tend to be temporally correlated, and such correlations can be acquired by adversaries. In this paper, we investigate the potential privacy loss of a traditional DP mechanism under temporal correlations. First, we analyze the privacy leakage of a DP mechanism under temporal correlation that can be modeled using Markov Chain. Our analysis reveals that, the event-level privacy loss of a DP mechanism may increase over time. We call the unexpected privacy loss temporal privacy leakage (TPL). Although TPL may increase over time, we find that its supremum may exist in some cases. Second, we design efficient algorithms for calculating TPL. Third, we propose data releasing mechanisms that convert any existing DP mechanism into one against TPL. Experiments confirm that our approach is efficient and effective.

中文翻译：

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在时间相关性下量化连续数据发布中的差异隐私

差分隐私（DP）作为严格的隐私框架受到越来越多的关注。现有的许多研究采用传统的DP机制（如拉普拉斯机制）作为原语在每个时间点不断释放隐私数据以保护隐私（即事件级隐私），假设不同时间点的数据是独立的，或者对手不知道数据之间的相关性。然而，连续生成的数据往往在时间上是相关的，而这种相关性可以被对手获取。在本文中，我们研究了时间相关性下传统 DP 机制的潜在隐私损失。首先，我们分析了可以使用马尔可夫链建模的时间相关性下 DP 机制的隐私泄漏。我们的分析表明，DP 机制的事件级隐私损失可能会随着时间的推移而增加。我们将意外的隐私损失称为时间隐私泄漏（TPL）。尽管 TPL 可能会随着时间的推移而增加，但我们发现在某些情况下可能存在其最高值。其次，我们设计了有效的算法来计算 TPL。第三，我们提出了数据发布机制，将任何现有的 DP 机制转换为一种反对 TPL 的机制。实验证实我们的方法是有效的。