  Smart meters, devices measuring the electricity and gas consumption of a
household, are currently being deployed at a fast rate throughout the world.
The data they collect are extremely useful, including in the fight against
climate change. However, these data and the information that can be inferred
from them are highly sensitive. Re-pseudonymization, i.e., the frequent
replacement of random identifiers over time, is widely used to share smart
meter data while mitigating the risk of re-identification. We here show how, in
spite of re-pseudonymization, households' consumption records can be pieced
together with high accuracy in large-scale datasets. We propose the first deep
learning-based profiling attack against re-pseudonymized smart meter data. Our
attack combines neural network embeddings, which are used to extract features
from weekly consumption records and are tailored to the smart meter
identification task, with a nearest neighbor classifier. We evaluate six neural
networks architectures as the embedding model. Our results suggest that the
Transformer and CNN-LSTM architectures vastly outperform previous methods as
well as other architectures, successfully identifying the correct household
73.4% of the time among 5139 households based on electricity and gas
consumption records (54.5% for electricity only). We further show that the
features extracted by the embedding model maintain their effectiveness when
transferred to a set of users disjoint from the one used to train the model.
Finally, we extensively evaluate the robustness of our results. Taken together,
our results strongly suggest that even frequent re-pseudonymization strategies
can be reversed, strongly limiting their ability to prevent re-identification
in practice.
