k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
MyLifeBits: a personal database for everything
Communications of the ACM - Personal information management
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Revisiting the uniqueness of simple demographics in the US population
Proceedings of the 5th ACM workshop on Privacy in electronic society
Protecting privacy in recorded conversations
PAIS '08 Proceedings of the 2008 international workshop on Privacy and anonymity in information society
Towards trajectory anonymization: a generalization-based approach
SPRINGL '08 Proceedings of the SIGSPATIAL ACM GIS 2008 International Workshop on Security and Privacy in GIS and LBS
A Framework for Ubiquitous Content Sharing
IEEE Pervasive Computing
Private memoirs of a smart meter
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
SOCIALCOM '10 Proceedings of the 2010 IEEE Second International Conference on Social Computing
Privacy protected life-context-aware alert by simplified sound spectrogram from microphone sensor
CASEMANS '11 Proceedings of the 5th ACM International Workshop on Context-Awareness for Self-Managing Systems
Analysis of the impact of data granularity on privacy for the smart grid
Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society
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The Smart Grid approach enhances the power grid with information technology. Smart Meters are an important part of the Smart Grid. They record the energy consumption of households with a high-resolution and transfer consumption records to the energy provider in real time. Since they allow to infer personal information like the daily routine of the household members, Smart Meters are also a promising source for lifelogging. However, in liberalized energy markets, many different parties have access to these data. This puts the privacy of consumers at risk. In this paper, we analyze to which degree Smart Meter data, as collected by our industry partner, can be linked to its producer, using simple statistical measures. We devise features of the energy consumption, for example, the first peak of demand in the morning, and we describe an analytical framework that quantifies how well these features can identify households. Finally, we conduct a study with 60,480 energy-consumption records from 180 households. Our study shows that 68 % of the records can be re-identified with simple means already. This insight is important for Smart Grids, as it emphasizes the need for research and use of anonymization techniques for the Smart Grid.