This brief focuses on the current research on security and privacy preservation in smart grids. Along with a review of the existing works, this brief includes fundamental system models, possible frameworks, useful performance, and future research directions. It explores privacy preservation demand response with adaptive key evolution, secure and efficient Merkle tree based authentication, and fine-grained keywords comparison in the smart grid auction market. By examining the current and potential security and privacy threats, the author equips readers to understand the developing issues in smart grids. The brief is designed for researchers and professionals working with computer communication networks and smart grids. Graduate students interested in networks and communication engineering will also find the brief an essential resource.
This book provides an overview of security and privacy issues in smart grid communications, as well as the challenges in addressing these issues. It also introduces several privacy enhancing aggregation techniques including multidimensional data aggregation, subset data aggregation, multifunctional data aggregation, data aggregation with fault tolerance, data aggregation with differential privacy, and data aggregation with integrity protection. Offering a comprehensive exploration of various privacy preserving data aggregation techniques, this book is an exceptional resource for the academics, researchers, and graduate students seeking to exploit secure data aggregation techniques in smart grid communications and Internet of Things (IoT) scenarios.
This SpringerBrief addresses the main security concerns for smart grid, e.g., the privacy of electricity consumers, the exchanged messages integrity and confidentiality, the authenticity of participated parties, and the false data injection attacks. Moreover, the authors demonstrate in detail the various proposed techniques to secure the smart grid’s different communication networks and preserve the privacy of the involved. Over many years, power grid has generated electricity from central generators and distributed it in one direction from the generation stations to end-users; also, information is one directional so that the grid’s control center doesn’t get enough information about customers’ requirements and consequently can’t prevent electricity losses. So, the electricity grid is merged with information and communication technology to form smart grid. The main target of this incorporation is to connect different parties of power grid to exchange information about grid conditions and customers’ requirements, and consequently, improve the reliability and efficiency of electricity generation and distribution. That upgrade of the power grid exposes it to the cyber security threats that the communication networks suffer from, such as malicious attacks to forge the electricity consumption readings or price, extract personal information for residential consumers, such as daily habits and life style, or attack some grid’s resources and equipment availability using denial-of-service attacks. Also, novel threats are introduced in smart grid due to the power grid nature, such as false data injection attack, in which the adversary compromises several measurement units and injects false information about the grid conditions that mislead the grid’s control center to make wrong decisions for the grid and consequently impact on its stability and efficiency.
This book presents current research in privacy-preserving protocols for smart grids. It contains several approaches and compares them analytically and by means of simulation. In particular, the book introduces asymmetric DC-Nets, which offer an ideal combination of performance and features in comparison with homomorphic encryption; data anonymization via cryptographic protocols; and data obfuscation by means of noise injection or by means of the installation of storage banks. The author shows that this theory can be leveraged into several application scenarios, and how asymmetric DC-Nets are generalizations of additive homomorphic encryption schemes and abstractions of symmetric DC-Nets. The book provides the reader with an understanding about smart grid scenarios, the privacy problem, and the mathematics and algorithms used to solve it.
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.
"The smart grid is an outcome of integrating communication technologies with traditional electrical systems. This enables the collection of granular metering data from the customer domain for providing grid and billing functionalities. However, the data collection process exposes the grid to various cyberattacks, posing a significant threat to customer privacy. This is a critical concern for the smart grid community and has hindered the global adoption of the smart grid technology. Although aggregation-based frameworks show promise for sharing metering data with the Electrical Service Provider while maintaining customer privacy, existing aggregation-based frameworks have several limitations. Some of these limitations include a high computational overhead on resource-constrained smart meters, susceptibility to single points of compromise due to dependency on a centralized entity, lack of support for dynamic billing functionality, and the absence of integrity verification capabilities for spatial and temporal metering data. To address the aforementioned limitations, we propose a distributed privacy-preserving framework for the smart grid that utilizes secret sharing, commitments, and secure multiparty computation. The framework consists of smart meters employing secret sharing and commitments to outsource their data to multiple aggregating entities, known as Dedicated Aggregators. These Dedicated Aggregators utilize secure multiparty computation to perform spatial aggregation in a privacy-preserving manner and report the aggregated readings to the Electrical Service Provider. By offloading most computations to the Dedicated Aggregators, our framework ensures that it remains lightweight for the smart meters. The introduction of multiple Dedicated Aggregators also aids in mitigating concerns associated with single points of compromise. Additionally, we have adapted the framework to support temporal aggregation, enabling dynamic billing functionalities while preserving customer privacy. The temporal aggregation process is integrated with the spatial aggregation process, thus imposing no additional computational overhead on the smart meters. The framework is designed to cater to both semi-honest and malicious adversarial settings, and works even in the presence of a majority of dishonest Dedicated Aggregators. In the event that some Dedicated Aggregators deviate from the normal execution of computing spatial and/or temporal aggregation by making modifications to metering data, the Electrical Service Provider can detect and respond to such modifications in a privacy-preserving manner. This dissertation presents our proposed framework and conducts a comprehensive analysis of it under different configurations. We develop a proof of concept to illustrate the practicality of implementing our framework in a real-world setting. We also compare its performance with other related works in the literature, evaluating the end-to-end delay for spatial aggregation. Additionally, we analyze the computational overhead on the smart meters in an embedded environment for various framework designs. The resilience of our proposed framework is analyzed against security and privacy threats. Finally, we identify future research directions to extend the capabilities of our framework."--Abstract.
The Internet of Things (IoT) concept has emerged partly due to information and communication technology developments and societal needs, expanding the ability to connect numerous objects. The wide range of facilities enabled by IoT has generated a vast amount of data, making cybersecurity an imperative requirement for personal safety and for ensuring the sustainability of the IoT ecosystem. This book covers security and privacy research in the IoT domain, compiling technical and management approaches, addressing real-world problems, and providing practical advice to the industry. This book also includes a collection of research works covering key emerging trends in IoT security and privacy that span the entire IoT architecture layers, focusing on different critical IoT applications such as advanced metering infrastructure and smart grids, smart locks, and cyber-physical systems. The provided state-of-the-art body of knowledge is essential for researchers, practitioners, postgraduate students, and developers interested in the security and privacy of the IoT paradigm, IoT-based systems, and any related research discipline. This book is a valuable companion and comprehensive reference for postgraduate and senior undergraduate students taking an advanced IoT security and privacy course.
This SpringerBrief covers the security and privacy challenges in fog computing, and proposes a new secure and privacy-preserving mechanisms to resolve these challenges for securing fog-assisted IoT applications. Chapter 1 introduces the architecture of fog-assisted IoT applications and the security and privacy challenges in fog computing. Chapter 2 reviews several promising privacy-enhancing techniques and illustrates examples on how to leverage these techniques to enhance the privacy of users in fog computing. Specifically, the authors divide the existing privacy-enhancing techniques into three categories: identity-hidden techniques, location privacy protection and data privacy enhancing techniques. The research is of great importance since security and privacy problems faced by fog computing impede the healthy development of its enabled IoT applications. With the advanced privacy-enhancing techniques, the authors propose three secure and privacy-preserving protocols for fog computing applications, including smart parking navigation, mobile crowdsensing and smart grid. Chapter 3 introduces identity privacy leakage in smart parking navigation systems, and proposes a privacy-preserving smart parking navigation system to prevent identity privacy exposure and support efficient parking guidance retrieval through road-side units (fogs) with high retrieving probability and security guarantees. Chapter 4 presents the location privacy leakage, during task allocation in mobile crowdsensing, and propose a strong privacy-preserving task allocation scheme that enables location-based task allocation and reputation-based report selection without exposing knowledge about the location and reputation for participators in mobile crowdsensing. Chapter 5 introduces the data privacy leakage in smart grid, and proposes an efficient and privacy-preserving smart metering protocol to allow collectors (fogs) to achieve real-time measurement collection with privacy-enhanced data aggregation. Finally, conclusions and future research directions are given in Chapter 6. This brief validates the significant feature extension and efficiency improvement of IoT devices without sacrificing the security and privacy of users against dishonest fog nodes. It also provides valuable insights on the security and privacy protection for fog-enabled IoT applications. Researchers and professionals who carry out research on security and privacy in wireless communication will want to purchase this SpringerBrief. Also, advanced level students, whose main research area is mobile network security will also be interested in this SpringerBrief.
Modern advances in sensor, computing, and communication technologies enable various smart grid applications which highlight the vulnerability that requires novel approaches to the field of cybersecurity. While substantial numbers of technologies have been adopted to protect cyber attacks in smart grid, there lacks a comprehensive review of the implementations, impacts, and solutions of cyber attacks specific to the smart grid. In this dissertation, we are motivated to evaluate the security requirements for the smart grid which include three main properties: confidentiality, integrity, and availability. First, we review the cyber-physical security of the synchrophasor network, which highlights all three aspects of security issues. Taking the synchrophasor network as an example, we give an overview of how to attack a smart grid network. We test three types of attacks and show the impact of each attack consisting of denial-of-service attack, sniffing attack, and false data injection attack. Next, we discuss how to protect against each attack. For protecting availability, we examine possible defense strategies for the associated vulnerabilities. For protecting data integrity, a small-scale prototype of secure synchrophasor network is presented with different cryptosystems. Besides, a deep learning based time-series anomaly detector is proposed to detect injected measurement. Our approach observes both data measurements and network traffic features to jointly learn system states and can detect attacks when state vector estimator fails. For protecting data confidentiality, we propose privacy-preserving algorithms for two important smart grid applications. 1) A distributed privacy-preserving quadratic optimization algorithm to solve Security Constrained Optimal Power Flow (SCOPF) problem. The SCOPF problem is decomposed into small subproblems using the Alternating Direction Method of Multipliers (ADMM) and gradient projection algorithms. 2) We use Paillier cryptosystem to secure the computation of the power system dynamic simulation. The IEEE 3-Machine 9-Bus System is used to implement and demonstrate the proposed scheme. The security and performance analysis of our implementations demonstrate that our algorithms can prevent chosen-ciphertext attacks at a reasonable cost.
This book introduces big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as a huge amount of data collection are first discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this book. By dealing with a huge amount of data from electricity networks, meteorological information system, geographical information system, etc., many benefits can be brought to the existing power system and improve customer service as well as social welfare in the era of big data. However, to advance the applications of big data analytics in real smart grids, many issues such as techniques, awareness, and synergies have to be overcome. This book provides deployment of semantic technologies in data analysis along with the latest applications across the field such as smart grids.
