Smart Grids: Security and Privacy Issues (eBook)

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.

Preface 5
Features 5
Intended Audience 6
Acknowledgments 7
Contents 8
Biography 11
Kianoosh G. Boroojeni 11
M. Hadi Amini 11
S.S. Iyengar 12
1 Overview of the Security and Privacy Issues in Smart Grids 13
1.1 Security Issues in Smart Grid 13
1.2 Physical Network Security 15
1.3 Information Network Security 18
1.3.1 Detection Mechanisms 19
1.3.2 Mitigation Mechanisms 19
1.4 Privacy Issues in Smart Grids 20
1.4.1 k-Anonymity Cloaking 21
1.4.2 Location Obfuscation 21
1.4.3 Location Privacy Quantification and Formalization 22
1.5 Book Structure and Outlook 22
References 23
Part I Physical Network Security 29
2 Reliability in Smart Grids 30
2.1 Introduction 30
2.2 Preliminaries on Reliability Quantification 33
Balancing Supply and Demand 33
Demand Exceeding the Supply 34
Supply Exceeding the Demand 34
2.3 System Adequacy Quantification 34
2.4 Congestion Prevention: An Economic Dispatch Algorithm 35
2.5 Summary and Conclusion 36
References 39
3 Error Detection of DC Power Flow Using State Estimation 41
3.1 Introduction 41
3.2 Preliminaries of the DC Power Flow and State Estimation 43
3.2.1 Introduction to State Estimation 45
3.3 Minimum-Variance Unbiased Estimator (MVUE) 46
3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation 47
3.3.2 Linear Model 47
3.3.3 Generalized Linear Model for State Estimation 48
3.4 Bayesian-Based LMMSE Estimator for DC Power Flow Estimation 49
3.4.1 Linear Model 49
3.4.2 Bayesian Linear Model 50
3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation 50
3.4.4 Bayesian-Based Linear Estimator for DC Power Flow 50
3.4.5 Recursive Bayesian-Based DC Power Flow Estimation Approach for DC Power Flow Estimation 51
3.5 Error Detection Using Sparse Vector Recovery 52
3.5.1 Sparse Vector Recovery 53
3.5.2 Proposed Sparsity-Based DC Power Flow Estimation 54
3.5.3 Case Study and Discussion 56
Simulation Results 56
References 58
4 Bad Data Detection 62
4.1 Preliminaries on Falsification Detection Algorithms 62
4.1.1 Autocorrelation Function (ACF) 63
4.2 Time Series Modeling of Load Power 63
4.2.1 Outline of the Proposed Methodology 63
4.2.2 Seasonality 65
4.2.3 Fitting the AR and MA Models 68
4.3 Case Study 68
4.3.1 Stabilizing the Variance 69
4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving-Average Elements 70
4.3.2.1 Real-Time Forecasting Model 70
4.3.2.2 Day-Ahead Forecasting Model 72
4.3.3 Model Fine-Tuning and Evaluation 73
4.4 Summary and Conclusion 77
References 77
Part II Information Network Security 78
5 Cloud Network Data Security 79
5.1 Introduction 79
Related Work 80
Detection Mechanisms 80
Mitigation Mechanisms 81
5.2 Data Security Protection in Cloud-Connected Smart Grids 81
5.2.1 Simulation Scheme 86
5.2.2 Simulation Results 87
5.3 Summary and Outlook 88
References 89
Part III Privacy Preservation 91
6 End-User Data Privacy 92
6.1 Introduction 92
6.2 Preliminaries to Privacy Preservation Methods 94
6.2.1 k-Anonymity Cloaking 94
6.2.2 Location Obfuscation 95
6.2.3 Preliminary Definitions 96
6.2.3.1 Location-Based Service 96
6.2.3.2 Location Privacy-Preserving Mechanism 96
6.2.3.3 Adversary 97
6.3 Privacy Preservation: Location Obfuscation Methods 97
6.4 Summary and Conclusion 98
References 98
7 Mobile User Data Privacy 100
7.1 Introduction 101
7.2 Preliminaries on Mobile Nodes Trajectory Privacy 102
7.2.1 Location-Based Service 103
7.2.2 Location Privacy-Preserving Mechanism 103
7.2.3 Adversary 104
7.3 Privacy Preservation Quantification: Probabilistic Model 104
7.4 A Vernoi-Based Location Obfuscation Method 107
7.4.1 A Stochastic Model of the Node Movement 109
7.4.2 Proposed Scheme for a Mobile Node 110
7.4.3 Computing the Instantaneous Privacy Level 111
7.4.4 Concealing the Movement Path 114
7.5 Summary and Conclusion 115
References 116
Index 118