TY - JOUR
T1 - MDMS
T2 - Efficient and Privacy-Preserving Multidimension and Multisubset Data Collection for AMI Networks
AU - Alsharif, Ahmad
AU - Nabil, Mahmoud
AU - Sherif, Ahmed
AU - Mahmoud, Mohamed
AU - Song, Min
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - Advanced metering infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers' activities. Privacy-preserving data aggregation techniques have been used to preserve consumers' privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this article, we propose an efficient and privacy-preserving multidimensional and multisubset data collection scheme, named 'MDMS. ' In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks' gateways using the encrypted readings and following the dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers' readings.
AB - Advanced metering infrastructure (AMI) networks allow utility companies to collect fine-grained power consumption data of electricity consumers for load monitoring and energy management. This brings serious privacy concerns since the fine-grained power consumption data can expose consumers' activities. Privacy-preserving data aggregation techniques have been used to preserve consumers' privacy while allowing the utility to obtain only the consumers total consumption. However, most of the existing schemes do not consider the multidimensional nature of power consumption in which electricity consumption can be categorized based on the consumption type. They also do not consider multisubset data collection in which the utility should be able to obtain the number of consumers whose consumption lies within a specific consumption range, and the overall consumption of each set of consumers. In this article, we propose an efficient and privacy-preserving multidimensional and multisubset data collection scheme, named 'MDMS. ' In MDMS, the utility can obtain the total power consumption as well as the number of consumers of each subset in each dimension. In addition, for better scalability, MDMS allows the utility to delegate bill computation to the AMI networks' gateways using the encrypted readings and following the dynamic prices in which electricity prices are different based on both the time and the consumption type. Moreover, MDMS uses lightweight operations in encryption, aggregation, and decryption resulting in low computation and communication overheads as given in our experimental results. Our security analysis demonstrates that MDMS is secure and can resist collusion attacks that aim to reveal the consumers' readings.
KW - Advanced metering infrastructure (AMI) networks
KW - multidimensional aggregation
KW - multisubset aggregation
KW - privacy preservation
KW - security
KW - smart grid
UR - http://www.scopus.com/inward/record.url?scp=85076742804&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85076742804&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2019.2938776
DO - 10.1109/JIOT.2019.2938776
M3 - Article
AN - SCOPUS:85076742804
VL - 6
SP - 10363
EP - 10374
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 6
M1 - 8822451
ER -