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.
1 Overview of the Security and Privacy Issues in Smart Grids 1.1 Security Issues in Smart Grid 1.2 Physical Network Security 1.3 Information Network Security 1.4 Privacy Issues in Smart Grids 1.5 Book Structure and Outlook I Physical Network Security 2 Reliability in Smart Grids 2.1 Introduction 2.2 Preliminaries on Reliability Quantification 2.3 System Adequacy Quantification 2.4 Congestion Prevention: An Economic Dispatch Algorithm 2.4.1 9-bus Test Network 2.4.2 IEEE 30-Bus Test Network 2.5 Summary and Conclusion 3 Error Detection of DC Power Flow using State Estimation 3.1 Introduction 3.2 Preliminaries of the DC Power Flow and State Estimation 3.2.1 Introduction to State Estimation 3.3 Minimum-Variance Unbiased Estimator (MVUE) 3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation 3.3.2 Linear Model 3.3.3 Generalized Linear Model for State Estimation 3.4 Bayesian-based LMMSE Estimator for DC Power Flow Estimation 3.4.1 Linear Model 3.4.2 Bayesian Li
near Model 3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation 3.4.4 Bayesian-based Linear Estimator for DC Power Flow 3.4.5 Recursive Bayesian-based DC power ow Estimation Approach for DC Power Flow Estimation 3.5 Error Detection Using Sparse Vector Recovery 3.5.1 Sparse Vector Recovery 3.5.2 Proposed Sparsity-based DC Power Flow Estimation 3.5.3 Case Study and Discussion 4 Bad Data Detection 4.1 Preliminaries on Falsification Detection Algorithms 4.1.1 Related Work 4.2 Time-Series Modeling of Load Power 4.2.1 Outline of the Proposed Methodology 4.2.2 Seasonality 4.2.3 Fitting the AR and MA Models 4.2.4 Forecast Validation Using Aikaike/Bayesian Information Criteria 4.3 Case Study 4.3.1 Stabilizing the Variance 4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving- Average Elements 4.3.3 Model Fine-Tuning and Evaluation 4.4 Summary and Conclusion II Information Network Security 5 Cloud Network Data Security 5.1 Introduction 5.2 Data Securit
y Protection in Cloud-connected Smart Grids 5.2.1 Simulation Scheme 5.2.2 Simulation Results 5.3 Summary and Outlook III Privacy Preservation 6 End-User Data Privacy 6.1 Introduction 6.2 Preliminaries to Privacy Preservation Methods 6.2.1 k-Anonymity Cloaking 6.2.2 Location Obfuscation 6.2.3 Preliminary Definitions 6.3 Privacy Preservation: Location Obfuscation Methods 6.4 Summary and Conclusion 7 Mobile User Data Privacy 7.1 Introduction 7.2 Preliminaries on Mobile Nodes Trajectory Privacy 7.3 Privacy Preservation Quantification: Probabilistic Model 7.4 A Vernoi-based Location Obfuscation Method 7.4.1 A Stochastic Model of the Node Movement 7.4.2 Proposed Scheme for A Mobile Node 7.4.3 Computing the Instantaneous Privacy Level 7.4.4 Concealing the Movement Path 7.5 Summary and Conclusion
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1 Overview of the Security and Privacy Issues in Smart Grids |
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1 | (18) |
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1.1 Security Issues in Smart Grid |
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1 | (2) |
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1.2 Physical Network Security |
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3 | (3) |
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1.3 Information Network Security |
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6 | (2) |
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1.3.1 Detection Mechanisms |
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7 | (1) |
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1.3.2 Mitigation Mechanisms |
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7 | (1) |
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1.4 Privacy Issues in Smart Grids |
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8 | (2) |
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1.4.1 k-Anonymity Cloaking |
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9 | (1) |
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1.4.2 Location Obfuscation |
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9 | (1) |
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1.4.3 Location Privacy Quantification and Formalization |
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10 | (1) |
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1.5 Book Structure and Outlook |
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10 | (9) |
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11 | (8) |
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Part I Physical Network Security |
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2 Reliability in Smart Grids |
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19 | (12) |
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19 | (3) |
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2.2 Preliminaries on Reliability Quantification |
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22 | (1) |
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2.3 System Adequacy Quantification |
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23 | (1) |
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2.4 Congestion Prevention: An Economic Dispatch Algorithm |
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24 | (1) |
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2.5 Summary and Conclusion |
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25 | (6) |
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28 | (3) |
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3 Error Detection of DC Power Flow Using State Estimation |
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31 | (22) |
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31 | (2) |
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3.2 Preliminaries of the DC Power Flow and State Estimation |
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33 | (3) |
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3.2.1 Introduction to State Estimation |
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35 | (1) |
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3.3 Minimum-Variance Unbiased Estimator (MVUE) |
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36 | (3) |
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3.3.1 Measurement Error Representation in the Linear DC Power Flow Equation |
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37 | (1) |
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37 | (1) |
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3.3.3 Generalized Linear Model for State Estimation |
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38 | (1) |
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3.4 Bayesian-Based LMMSE Estimator for DC Power Flow Estimation |
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39 | (3) |
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39 | (1) |
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3.4.2 Bayesian Linear Model |
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40 | (1) |
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3.4.3 Maximum Likelihood Estimator for DC Power Flow Estimation |
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40 | (1) |
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3.4.4 Bayesian-Based Linear Estimator for DC Power Flow |
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40 | (1) |
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3.4.5 Recursive Bayesian-Based DC Power Flow Estimation Approach for DC Power Flow Estimation |
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41 | (1) |
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3.5 Error Detection Using Sparse Vector Recovery |
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42 | (11) |
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3.5.1 Sparse Vector Recovery |
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43 | (1) |
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3.5.2 Proposed Sparsity-Based DC Power Flow Estimation |
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44 | (2) |
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3.5.3 Case Study and Discussion |
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46 | (2) |
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48 | (5) |
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53 | (18) |
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4.1 Preliminaries on Falsification Detection Algorithms |
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53 | (1) |
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4.1.1 Autocorrelation Function (ACF) |
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54 | (1) |
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4.2 Time Series Modeling of Load Power |
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54 | (5) |
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4.2.1 Outline of the Proposed Methodology |
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54 | (2) |
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56 | (3) |
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4.2.3 Fitting the AR and MA Models |
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59 | (1) |
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59 | (9) |
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4.3.1 Stabilizing the Variance |
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60 | (1) |
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4.3.2 Fitting the Stationary Signal to a Model with Autoregressive and Moving-Average Elements |
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61 | (3) |
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4.3.3 Model Fine-Tuning and Evaluation |
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64 | (4) |
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4.4 Summary and Conclusion |
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68 | (3) |
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68 | (3) |
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Part II Information Network Security |
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5 Cloud Network Data Security |
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71 | (14) |
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71 | (2) |
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5.2 Data Security Protection in Cloud-Connected Smart Grids |
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73 | (7) |
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78 | (1) |
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79 | (1) |
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80 | (5) |
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81 | (4) |
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Part III Privacy Preservation |
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85 | (8) |
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85 | (2) |
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6.2 Preliminaries to Privacy Preservation Methods |
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87 | (3) |
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6.2.1 k-Anonymity Cloaking |
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87 | (1) |
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6.2.2 Location Obfuscation |
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88 | (1) |
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6.2.3 Preliminary Definitions |
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89 | (1) |
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6.3 Privacy Preservation: Location Obfuscation Methods |
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90 | (1) |
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6.4 Summary and Conclusion |
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91 | (2) |
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91 | (2) |
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7 Mobile User Data Privacy |
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93 | (18) |
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94 | (1) |
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7.2 Preliminaries on Mobile Nodes Trajectory Privacy |
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95 | (2) |
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7.2.1 Location-Based Service |
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96 | (1) |
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7.2.2 Location Privacy-Preserving Mechanism |
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96 | (1) |
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97 | (1) |
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7.3 Privacy Preservation Quantification: Probabilistic Model |
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97 | (3) |
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7.4 A Vernoi-Based Location Obfuscation Method |
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100 | (8) |
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7.4.1 A Stochastic Model of the Node Movement |
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102 | (1) |
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7.4.2 Proposed Scheme for a Mobile Node |
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103 | (1) |
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7.4.3 Computing the Instantaneous Privacy Level |
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104 | (3) |
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7.4.4 Concealing the Movement Path |
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107 | (1) |
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7.5 Summary and Conclusion |
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108 | (3) |
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109 | (2) |
| Index |
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111 | |
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.
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