Abstract
The improvement of energy efficiency is an important target on all levels of society. It is best achieved on the basis of locally and temporally fine-grained measurement data for identifying unnecessary use of energy. However, at the same time such fine-grained measurements allow deriving information about the persons using the energy. In this paper we describe our work towards a privacy preserving system for energy management. Our solution follows the privacy by design paradigm and uses attribute-based cryptography and virtualization to increase security.
Cite
CITATION STYLE
Kinkelin, H., von Maltitz, M., Peter, B., Kappler, C., Niedermayer, H., & Carle, G. (2015). Privacy preserving energy management. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8852, pp. 35–42). Springer Verlag. https://doi.org/10.1007/978-3-319-15168-7_5
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