Smart Grids: Security and Privacy Issues

Softcover Nachdruck of the original 1. Auflage 2017
ISBN: 978-3-319-83197-8
Verlag: Springer

Buch, Englisch, 113 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 2058 g

This book provides a thorough treatment of privacy and security issues for researchers in the fields of smart grids, engineering, and computer science. It presents comprehensive insight to understanding the big picture of privacy and security challenges in both physical and information aspects of smart grids. The authors utilize an advanced interdisciplinary approach to address the existing security and privacy issues and propose legitimate countermeasures for each of them in the standpoint of both computing and electrical engineering. The proposed methods are theoretically proofed by mathematical tools and illustrated by real-world examples.

Zielgruppe

Research

Autoren/Hrsg.

Boroojeni, Kianoosh G.

Amini, M. Hadi

Iyengar, S. S.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Computersicherheit
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Technische Wissenschaften Energietechnik | Elektrotechnik Energieverteilung, Stromnetze
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung Netzwerksicherheit

Inhaltsverzeichnis

1 Overview of the Security and Privacy Issues in Smart Grids1.1 Security Issues in Smart Grid1.2 Physical Network Security1.3 Information Network Security1.4 Privacy Issues in Smart Grids1.5 Book Structure and Outlook
I Physical Network Security
2 Reliability in Smart Grids2.1 Introduction2.2 Preliminaries on Reliability Quantification2.3 System Adequacy Quantification2.4 Congestion Prevention: An Economic Dispatch Algorithm2.4.1 9-bus Test Network2.4.2 IEEE 30-Bus Test Network2.5 Summary and Conclusion
3 Error Detection of DC Power Flow using State Estimation3.1 Introduction3.2 Preliminaries of the DC Power Flow and State Estimation3.2.1 Introduction to State Estimation3.3 Minimum-Variance Unbiased Estimator (MVUE)3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation3.3.2 Linear Model3.3.3 Generalized Linear Model for State Estimation3.4 Bayesian-based LMMSE Estimator for DC Power Flow Estimation3.4.1 Linear Model3.4.2 Bayesian Linear Model3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation3.4.4 Bayesian-based Linear Estimator for DC Power Flow3.4.5 Recursive Bayesian-based DC power ow Estimation Approach for DC PowerFlow Estimation3.5 Error Detection Using Sparse Vector Recovery3.5.1 Sparse Vector Recovery3.5.2 Proposed Sparsity-based DC Power Flow Estimation3.5.3 Case Study and Discussion
4 Bad Data Detection4.1 Preliminaries on Falsification Detection Algorithms4.1.1 Related Work4.2 Time-Series Modeling of Load Power4.2.1 Outline of the Proposed Methodology4.2.2 Seasonality4.2.3 Fitting the AR and MA Models4.2.4 Forecast Validation Using Aikaike/Bayesian Information Criteria4.3 Case Study4.3.1 Stabilizing the Variance4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving-Average Elements4.3.3 Model Fine-Tuning and Evaluation4.4 Summary and Conclusion
II Information Network Security5 Cloud Network Data Security5.1 Introduction5.2 Data Security Protection in Cloud-connected Smart Grids5.2.1 Simulation Scheme5.2.2 Simulation Results5.3 Summary and Outlook
III Privacy Preservation6 End-User Data Privacy6.1 Introduction6.2 Preliminaries to Privacy Preservation Methods6.2.1 k-Anonymity Cloaking6.2.2 Location Obfuscation6.2.3 Preliminary Definitions6.3 Privacy Preservation: Location Obfuscation Methods6.4 Summary and Conclusion
7 Mobile User Data Privacy7.1 Introduction7.2 Preliminaries on Mobile Nodes Trajectory Privacy7.3 Privacy Preservation Quantification: Probabilistic Model7.4 A Vernoi-based Location Obfuscation Method7.4.1 A Stochastic Model of the Node Movement7.4.2 Proposed Scheme for A Mobile Node7.4.3 Computing the Instantaneous Privacy Level7.4.4 Concealing the Movement Path7.5 Summary and Conclusion

Über Autor(innen)

Kianoosh G. Boroojeni is a PhD candidate of computer science at FIU. He received his Computer Science B.Sc in University of Tehran, Iran (2012).research interests include network algorithms, cybersecurity, and optimization algorithms. He co-authored two books entitled "Mathematical Theories of Distributed Sensor Networks" (published by Springer) and "Oblivious Network Routing: Algorithms and Applications" (published by MIT Press). Currently, Kianoosh is collaborating with Dr. S.S. Iyengar on some security issues in the context of cloud computing and smart grids.

M. Hadi Amini received the B.Sc. degree from the Sharif University of Technology, Tehran, Iran, in 2011, and the M.Sc. degree from Tarbiat Modares University, Tehran, in 2013, both in Electrical Engineering. He also received the M.Sc. degree in Electrical and Computer Engineering from Carnegie Mellon University in 2015. He is currently pursuing the dual-degree Ph.D. in Electrical and Computer Engineering with the Department of Electrical and Computer Engineering, Carnegie Mellon University (CMU), Pittsburgh, PA, USA and Computer Science and Technology with the Sun Yat-sen University-CMU Joint Institute of Engineering, School of Electronics and Information Technology, Guangzhou, Guangdong, China. He is also with SYSU-CMU Shunde International Joint Research Institute, Shunde, Guangdong, China. Hadi serves as reviewer for several high impact journals and international conferences and symposiums in the field of smart grid. He has published more than 40 papers in refereed journal and international conferences in the smart grid related areas. He has been awarded the 5-year scholarship from the SYSU-CMU Joint Institute of Engineering in 2014, sustainable mobility summer fellowship from Massachusetts Institute of Technology (MIT) office of sustainability in 2015, and the deans honorary award from the president of Sharif University of Technology in 2007. His current research interests include smart grids, electric vehicles, distributed optimization methods in interdependent power and transportation networks, and state estimation.

S.S. Iyengar is a leading researcher in the fields of distributed sensor networks, computational robotics, and oceanographic applications, and is perhaps best known for introducing novel data structures and algorithmic techniques for large scale computations in sensor technologies and image processing applications. He is currently the Director and Ryder Professor at Florida International University's School of Computing and Information Sciences in Miami, FL. He has published more than 500 research papers and has authored or co-authored 12 textbooks and edited 10 others. Iyengar is a Member of the European Academy of Sciences, a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of National Academy of Inventors (NAI) a Fellow of the Association of Computing Machinery (ACM), a Fellow of the American Association for the Advancement of Science(AAAS), and Fellow of the Society for Design and Process Science (SDPS). He has received the Distinguished Alumnus Award of the Indian Institute of Science. In 1998, he was awarded the IEEE Computer Society's Technical Achievement Awardand is an IEEE Golden Core Member. Professor Iyengar is an IEEE Distinguished Visitor, SIAM Distinguished Lecturer, and ACM National Lecturer. In 2006, his paper entitled, A Fast Parallel Thinning Algorithm for the Binary Image Skeletonization, was the most frequently read article in the month of January in the International Journal of High Performance Computing Applications. His innovative work called the Brooks-Iyengar algorithm along with Prof. Richard Brooks from Clemson University is applied in industries and some real-world applications.