Privacy-Preserving Hierarchical State Estimation in Untrustworthy Cloud Environments

Hierarchical state estimation (HSE) is often deployed to evaluate the states of an interconnected power system from telemetered measurements. By HSE, each low-level control center (LCC) takes charge of the estimation of its internal states, whereas a trusted high-level control center (HCC) assumes the coordination of boundary states. However, a trusted HCC may not always exist in practice; a cloud server can take the role of an HCC in case no such facility is available. Since it is prohibited to release sensitive power grid data to untrustworthy cloud environments, considerations need to be given to avoid breaches of LCCs’ privacy when outsourcing the coordination tasks to the cloud server. To this end, this article proposes a privacy-preserving HSE framework, which rearranges the regular HSE procedure to integrate a degree-2 variant of the Thresholded Paillier Cryptosystem (D2TPC). Attributed to D2TPC, computations by the cloud-based HCC can be conducted entirely in the ciphertext space. Even if the HCC and some LCCs conspire together to share the information they have, the privacy of non-conspiring LCCs is still assured. Experiments on various scales of test systems demonstrate a high level of accuracy, efficiency, and scalability of the proposed framework.

[1]  Wenchuan Wu,et al.  An Adaptive Distributed Quasi-Newton Method for Power System State Estimation , 2019, IEEE Transactions on Smart Grid.

[2]  Dequan Li,et al.  Privacy-Preservation in Online Distributed Dual Averaging optimization , 2019, 2019 Chinese Control Conference (CCC).

[3]  Jing Yu,et al.  Distributed State Estimation of Multi-region Power System based on Consensus Theory , 2019, Energies.

[4]  Kui Ren,et al.  Security and Cloud Outsourcing Framework for Economic Dispatch , 2018, IEEE Transactions on Smart Grid.

[5]  Minghui Zhu,et al.  Privacy preserving distributed optimization using homomorphic encryption , 2018, Autom..

[6]  Jiming Chen,et al.  Secure Kalman Filter State Estimation by Partially Homomorphic Encryption , 2018, 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS).

[7]  Lang Tong,et al.  Hierarchical Multi-Area State Estimation via Sensitivity Function Exchanges , 2017, IEEE Transactions on Power Systems.

[8]  Antonello Monti,et al.  An Efficient and Accurate Solution for Distribution System State Estimation with Multiarea Architecture , 2017, IEEE Transactions on Instrumentation and Measurement.

[9]  Ismail Güvenç,et al.  Secure Data Obfuscation Scheme to Enable Privacy-Preserving State Estimation in Smart Grid AMI Networks , 2016, IEEE Internet of Things Journal.

[10]  D. Catalano,et al.  Using Linearly-Homomorphic Encryption to Evaluate Degree-2 Functions on Encrypted Data , 2015, CCS.

[11]  Henrik Sandberg,et al.  Differentially private state estimation in distribution networks with smart meters , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[12]  Lang Tong,et al.  Probabilistic Forecasting of Real-Time LMP and Network Congestion , 2015, IEEE Transactions on Power Systems.

[13]  Francisco Javier González-Serrano,et al.  State estimation using an extended Kalman filter with privacy-protected observed inputs , 2014, 2014 IEEE International Workshop on Information Forensics and Security (WIFS).

[14]  Xuemin Shen,et al.  EPPDR: An Efficient Privacy-Preserving Demand Response Scheme with Adaptive Key Evolution in Smart Grid , 2014, IEEE Transactions on Parallel and Distributed Systems.

[15]  Chun-I Fan,et al.  Privacy-Enhanced Data Aggregation Scheme Against Internal Attackers in Smart Grid , 2014, IEEE Transactions on Industrial Informatics.

[16]  Robbert van Renesse,et al.  Toward a reliable, secure and fault tolerant smart grid state estimation in the cloud , 2013, 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT).

[17]  L. Wehenkel,et al.  Contingency Ranking With Respect to Overloads in Very Large Power Systems Taking Into Account Uncertainty, Preventive, and Corrective Actions , 2013, IEEE Transactions on Power Systems.

[18]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[19]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[20]  Mani B. Srivastava,et al.  Cooperative state estimation for preserving privacy of user behaviors in smart grid , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[21]  H. Vincent Poor,et al.  Competitive privacy in the smart grid: An information-theoretic approach , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).

[22]  Antonio Gómez Expósito,et al.  A Multilevel State Estimation Paradigm for Smart Grids , 2011, Proceedings of the IEEE.

[23]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[24]  Peng Liu,et al.  Secure Information Aggregation for Smart Grids Using Homomorphic Encryption , 2010, 2010 First IEEE International Conference on Smart Grid Communications.

[25]  Elaine B. Barker,et al.  Recommendation for Key Management - Part 3: Application-Specific Key Management Guidance , 2009 .

[26]  A. Abur,et al.  Multi area state estimation using synchronized phasor measurements , 2005, IEEE Transactions on Power Systems.

[27]  I. Damgård,et al.  A Generalisation, a Simplification and some Applications of Paillier’s Probabilistic Public-Key System , 2000 .

[28]  A. Monticelli,et al.  Electric power system state estimation , 2000, Proceedings of the IEEE.

[29]  Ivan Damgård,et al.  Multiparty Computation from Threshold Homomorphic Encryption , 2000, EUROCRYPT.

[30]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[31]  Shin-Yeu Lin,et al.  A distributed state estimator for electric power systems , 1992 .

[32]  T. Van Cutsem,et al.  Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems , 1983, IEEE Power Engineering Review.

[33]  Ali Akbar Safavi,et al.  Private State Estimation for Cyber-physical Systems Using Semi-homomorphic Encryption , 2018 .

[34]  Rakesh Bobba,et al.  Cloud Computing for the Power Grid: From Service Composition to Assured Clouds , 2013, HotCloud.

[35]  S. Fienberg,et al.  Secure multiple linear regression based on homomorphic encryption , 2011 .

[36]  Mohammad Shahidehpour,et al.  Parallel and Distributed State Estimation , 2003 .