| 书目名称 | Differential Privacy for Dynamic Data |
| 编辑 | Jerome Le Ny |
| 视频video | |
| 概述 | Introduces readers to the emerging topic of privacy in the context of signal processing and control systems.Illustrates concepts, with case studies that analyze real-world public datasets.Provides a s |
| 丛书名称 | SpringerBriefs in Electrical and Computer Engineering |
| 图书封面 |  |
| 描述 | .This Springer brief provides the necessary foundations to understand differential privacy and describes practical algorithms enforcing this concept for the publication of real-time statistics based on sensitive data. Several scenarios of interest are considered, depending on the kind of estimator to be implemented and the potential availability of prior public information about the data, which can be used greatly to improve the estimators‘ performance. The brief encourages the proper use of large datasets based on private data obtained from individuals in the world of the Internet of Things and participatory sensing. For the benefit of the reader, several examples are discussed to illustrate the concepts and evaluate the performance of the algorithms described. These examples relate to traffic estimation, sensing in smart buildings, and syndromic surveillance to detect epidemic outbreaks.. |
| 出版日期 | Book 2020 |
| 关键词 | Privacy- Preserving Data Analysis; Real-Time Signal Processing; Systems and Control; Privacy Issues for |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-41039-1 |
| isbn_softcover | 978-3-030-41038-4 |
| isbn_ebook | 978-3-030-41039-1Series ISSN 2191-8112 Series E-ISSN 2191-8120 |
| issn_series | 2191-8112 |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 19:27:20
