Differential Privacy for Dynamic Data
Differential Privacy for Dynamic Data
复制标题
动态数据的差异隐私
DOI：
10.1007/978-3-030-41039-1
复制
发表时间：
2020
影响因子：
1.7
通讯作者：
J. L. Ny
中科院分区：
文献类型：
--
作者：
J. L. Ny
智能电网或智能交通系统等新兴系统往往要求终端用户应用程序不断向执行监控或控制任务的外部数据聚集器发送信息。这可能会导致用户失去隐私，以换取应用程序提供的好处。在这一趋势的推动下，我们在系统理论的背景下引入了隐私问题，并解决了释放尊重用户数据流隐私的过滤信号的问题。我们的方法依赖于数据库文献中的一个正式的隐私概念，称为差异隐私，它提供了强大的隐私保证，防止攻击者使用任意的辅助信息。这次演讲将讨论一些场景，在这些场景中，设计具有隐私约束的过滤器和动态估计器是很重要的，并展示来自系统和控制理论的工具如何帮助完成这项任务。
Emerging systems such as smart grids or intelligent transportation systems often require end-user applications to continuously send information to external data aggregators performing monitoring or control tasks. This can result in an undesirable loss of privacy for the users in exchange of the benefits provided by the application. Motivated by this trend, we introduce privacy concerns in a system theoretic context, and address the problem of releasing filtered signals that respect the privacy of the users' data streams. Our approach relies on a formal notion of privacy from the database literature, called differential privacy, which provides strong privacy guarantees against adversaries with arbitrary side information. This talk will discuss a number of scenarios where designing filters and dynamic estimators with privacy constraints is important, and show how tools from systems and control theory can help with this task.