Multi-vendor penetration testing in the advanced metering infrastructure
Proceedings of the 26th Annual Computer Security Applications Conference
Embedded firmware diversity for smart electric meters
HotSec'10 Proceedings of the 5th USENIX conference on Hot topics in security
Plug-in privacy for smart metering billing
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
Role of context-awareness for demand response mechanisms
ICT-GLOW'11 Proceedings of the First international conference on Information and communication on technology for the fight against global warming
Protecting consumer privacy from electric load monitoring
Proceedings of the 18th ACM conference on Computer and communications security
Smart metering de-pseudonymization
Proceedings of the 27th Annual Computer Security Applications Conference
Conditional access smart meter privacy based on multi-resolution wavelet analysis
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
S2A: secure smart household appliances
Proceedings of the second ACM conference on Data and Application Security and Privacy
Fault-tolerant privacy-preserving statistics
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
Minimizing private data disclosures in the smart grid
Proceedings of the 2012 ACM conference on Computer and communications security
Neighborhood watch: security and privacy analysis of automatic meter reading systems
Proceedings of the 2012 ACM conference on Computer and communications security
V2GPriv: vehicle-to-grid privacy in the smart grid
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Occupancy inferencing from non-intrusive data sources
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Current and upcoming demand-response systems provide increasingly detailed power-consumption data to utilities and a growing array of players angling to assist consumers in understanding and managing their energy use. The granularity of this data, as well as new players' entry into the energy market, creates new privacy concerns. The detailed per-household consumption data that advanced metering systems generate reveals information about in-home activities that such players can mine and combine with other readily available information to discover more about occupants' activities. The authors explore the technological aspects of this claim, focusing on the ways in which personally identifying information can be collected and repurposed. Their results show that, even with relatively unsophisticated hardware and data-extraction algorithms, some information about occupant behavior can be estimated with a high degree of accuracy. The authors propose a disclosure metric to aid in quantifying the impact of data collection on in-home privacy and construct an example metric for their experiment.