An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Unified Architecture for Large-Scale Attested Metering
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Inferring Personal Information from Demand-Response Systems
IEEE Security and Privacy
Private memoirs of a smart meter
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof
IEEE Transactions on Consumer Electronics
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Fault-tolerant privacy-preserving statistics
PETS'12 Proceedings of the 12th international conference on Privacy Enhancing Technologies
V2GPriv: vehicle-to-grid privacy in the smart grid
CSS'12 Proceedings of the 4th international conference on Cyberspace Safety and Security
Sally: a tool for embedding strings in vector spaces
The Journal of Machine Learning Research
Implementation of privacy-friendly aggregation for the smart grid
Proceedings of the first ACM workshop on Smart energy grid security
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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Consumption traces collected by Smart Meters are highly privacy sensitive data. For this reason, current best practice is to store and process such data in pseudonymized form, separating identity information from the consumption traces. However, even the consumption traces alone may provide many valuable clues to an attacker, if combined with limited external indicators. Based on this observation, we identify two attack vectors using anomaly detection and behavior pattern matching that allow effective depseudonymization. Using a practical evaluation with real-life consumption traces of 53 households, we verify the feasibility of our techniques and show that the attacks are robust against common countermeasures, such as resolution reduction or frequent re-pseudonymization.