TY - JOUR
T1 - RPMDA
T2 - Robust and Privacy-Enhanced Multidimensional Data Aggregation Scheme for Fog-Assisted Smart Grids
AU - Liu, Jingwei
AU - Wang, Haoze
AU - Bao, Jiajia
AU - Sun, Rong
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2014 IEEE.
PY - 2024/5/1
Y1 - 2024/5/1
N2 - The increasing demand for intelligent management in modern power systems has emphasized the importance of smart grids, which facilitate real-time analysis and management through data aggregation. Fog computing provides efficient data processing and low-latency transmission for data aggregation. However, fog-assisted smart grids still face significant challenges, including privacy leakage, calculation limitations, and system stability issues. In response to these obstacles, we propose a robust and privacy-enhanced multidimensional data aggregation (RPMDA) scheme. Specifically, the Chinese remainder theorem is used to improve the efficiency of processing multidimensional data, combined with an innovative double-masking method to cope with secure data aggregation. For the purpose of reliable authentication, a conditional anonymous certificateless signature algorithm is designed in RPMDA, where the pseudonym generation mechanism ensures the conditional anonymity of smart meters (SMs). Besides, our scheme incorporates robustness, ensuring that the aggregated results remain unaffected even if SMs malfunction. Compared to the existing solutions, RPMDA shows superior performance while meeting security requirements.
AB - The increasing demand for intelligent management in modern power systems has emphasized the importance of smart grids, which facilitate real-time analysis and management through data aggregation. Fog computing provides efficient data processing and low-latency transmission for data aggregation. However, fog-assisted smart grids still face significant challenges, including privacy leakage, calculation limitations, and system stability issues. In response to these obstacles, we propose a robust and privacy-enhanced multidimensional data aggregation (RPMDA) scheme. Specifically, the Chinese remainder theorem is used to improve the efficiency of processing multidimensional data, combined with an innovative double-masking method to cope with secure data aggregation. For the purpose of reliable authentication, a conditional anonymous certificateless signature algorithm is designed in RPMDA, where the pseudonym generation mechanism ensures the conditional anonymity of smart meters (SMs). Besides, our scheme incorporates robustness, ensuring that the aggregated results remain unaffected even if SMs malfunction. Compared to the existing solutions, RPMDA shows superior performance while meeting security requirements.
KW - Data aggregation
KW - fault tolerance
KW - fog computing
KW - privacy-preserving
KW - smart grids
UR - http://www.scopus.com/inward/record.url?scp=85182357301&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85182357301&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2024.3352558
DO - 10.1109/JIOT.2024.3352558
M3 - Article
AN - SCOPUS:85182357301
VL - 11
SP - 16021
EP - 16032
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 9
ER -