The Internet of Things (IoT) promises to improve user utility by tuning applications to user behavior, but revealing the characteristics of a user's behavior presents a significant privacy risk. Our previous work has established the challenging requirements for anonymization to protect users' privacy in a Bayesian setting in which we assume a powerful adversary who has perfect knowledge of the prior distribution for each user's behavior. However, even sophisticated adversaries do not often have such perfect knowledge; hence, in this paper, we turn our attention to an adversary who must learn user behavior from past data traces of limited length. We also assume there exists dependency between data traces of different users, and the data points of each user are drawn from a normal distribution. Results on the lengths of training sequences and data sequences that result in a loss of user privacy are presented.

中文翻译：

---

依赖用户时间序列匹配导致的渐近隐私损失

物联网 (IoT) 承诺通过根据用户行为调整应用程序来提高用户效用，但揭示用户行为的特征会带来重大的隐私风险。我们之前的工作已经建立了具有挑战性的匿名化要求，以在贝叶斯设置中保护用户的隐私，其中我们假设一个强大的对手对每个用户的行为的先验分布有完美的了解。然而，即使是老练的对手也不经常拥有如此完美的知识。因此，在本文中，我们将注意力转向必须从有限长度的过去数据轨迹中学习用户行为的对手。我们还假设不同用户的数据轨迹之间存在依赖关系，并且每个用户的数据点都来自正态分布。