Side Channel Security of Smart Meter Data Compression Techniques

TY - GEN

T1 - Side Channel Security of Smart Meter Data Compression Techniques

AU - Fehér, Marcell

AU - Yazdani, Niloofar

AU - Aranha, Diego F.

AU - Lucani Rötter, Daniel Enrique

AU - Hansen, Morten Tranberg

AU - Vester, Flemming Enevold

PY - 2020

Y1 - 2020

N2 - Given the large and sustained growth in the number of smart meters for different applications, e.g., electricity, water or heat, effective data compression has become increasingly important.Although smart meters tend to encrypt payloads using state-of-the-art solutions, the packet length variability introduced by compression of the data can be exploited in a side channel attack to gain knowledge about the consumption of individual meters.For example, a meter reporting constant (e.g. zero) consumption can be compressed more than one reporting more erratic usage.An attacker may gain knowledge of behavioral patterns of a household, e.g., when is no one home, or company, e.g., active periods of production.This paper analyzes the correlation between compressed packet length and reported consumption of multiple signals and practical reporting periods for the DLMS standard using real (anonymized) smart meter measurements.We consider various built-in compressors and also propose new techniques that can both increase the compression and reduce this correlation.Our proposed schemes are particularly well suited for the increasingly popular case of high frequency reporting, e.g., reporting each measurement as it becomes available.

AB - Given the large and sustained growth in the number of smart meters for different applications, e.g., electricity, water or heat, effective data compression has become increasingly important.Although smart meters tend to encrypt payloads using state-of-the-art solutions, the packet length variability introduced by compression of the data can be exploited in a side channel attack to gain knowledge about the consumption of individual meters.For example, a meter reporting constant (e.g. zero) consumption can be compressed more than one reporting more erratic usage.An attacker may gain knowledge of behavioral patterns of a household, e.g., when is no one home, or company, e.g., active periods of production.This paper analyzes the correlation between compressed packet length and reported consumption of multiple signals and practical reporting periods for the DLMS standard using real (anonymized) smart meter measurements.We consider various built-in compressors and also propose new techniques that can both increase the compression and reduce this correlation.Our proposed schemes are particularly well suited for the increasingly popular case of high frequency reporting, e.g., reporting each measurement as it becomes available.

KW - Compression

KW - Data compression

KW - IoT

KW - Smart meters

KW - generalized deduplication

UR - http://www.scopus.com/inward/record.url?scp=85099445923&partnerID=8YFLogxK

U2 - 10.1109/SmartGridComm47815.2020.9302931

DO - 10.1109/SmartGridComm47815.2020.9302931

M3 - Article in proceedings

SN - 978-1-7281-6127-3

BT - 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

PB - IEEE

T2 - IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids

Y2 - 11 November 2020 through 13 November 2020

ER -