Exploiting DLMS/COSEM Data Compression To Learn Power Consumption Patterns

Smart electricity meters are widely deployed report power consumption automatically and frequently. However, the current compression methods have been suspected to leak information about the times when consumers are active, by mirroring spikes of power consumption in the compressed message size. In this paper we show that, compressed message sizes are indeed highly correlated with the underlying power use. We present a clustering-based method that allows a passive adversary monitoring encrypted network traffic to build and exploit power consumption profiles of homes. We evaluate the vulnerability of legacy compressors of the DLMS/COSEM standard as well as a recently proposed algorithm. Our results show that the novel algorithm not only provides higher compression potential, but results in the least information leakage. We evaluate our results on an real, anonymized dataset spanning 9 months and 95 households.