Differential Privacy for Dynamic Data
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Beschreibung
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.
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 systems-theoretic approach to network privacy avoiding the need to assimilate different algorithms and ideas from disparate sources
Autor*in
Jerome Le Ny
Themen in »Differential Privacy for Dynamic Data«
Privacy- Preserving Data Analysis Real-Time Signal Processing Systems and Control Privacy Issues for the Internet of Things Participatory Sensing Traffic Estimation Energy Consumption in Smart Grids Syndromic Surveillance