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In compressed sensing (CS) a signal x ∈ Rn is measured as y =A x + z, where A ∈ Rm×n (m<n) and z ∈ Rm denote the sensing matrix and measurement noise. The goal is to recover x from measurements y when m<n. CS is possible because we typically want to capture highly structured signals, and recovery algorithms take advantage of a signal’s structure to solve the under-determined system of linear equations. As in CS, data-compression codes take advantage of a signal’s structure to encode it efficiently. Structures used by compression codes are much more elaborate than those used by CS algorithms. Using more complex structures in CS, like those employed by data-compression codes, potentially leads to more efficient recovery methods requiring fewer linear measurements or giving better reconstruction quality. We establish connections between data compression and CS, giving CS recovery methods based on compression codes, which indirectly take advantage of all structures used by compression codes. This elevates the class of structures used by CS algorithms to those used by compression codes, leading to more efficient CS recovery methods.
Smart grids (SGs) promise to deliver dramatic improvements compared to traditional power grids thanks primarily to the large amount of data being exchanged and processed within the grid, which enables the grid to be monitored more accurately and at a much faster pace. The smart meter (SM) is one of the key devices that enable the SG concept by monitoring a household’s electricity consumption and reporting it to the utility provider (UP), i.e., the entity that sells energy to customers, or to the distribution system operator (DSO), i.e., the entity that operates and manages the grid. However, the very availability of rich and high-frequency household electricity consumption data, which enables a very efficient power grid management, also opens up unprecedented challenges on data security and privacy. To counter these threats, it is necessary to develop techniques that keep SM data private, and, for this reason, SM privacy has become a very active research area. The aim of this chapter is to provide an overview of the most significant privacy-preserving techniques for SM data, highlighting their main benefits and disadvantages.
This chapter focuses on critical infrastructures in the power grid, which often rely on Industrial Control Systems (ICS) to operate and are exposed to vulnerabilities ranging from physical damage to injection of information that appears to be consistent with industrial control protocols. This way, inﬁltration of ﬁrewalls protecting the control perimeter of the control network becomes a signiﬁcant tread. The goal of this chapter is to review identiﬁcation and intrusion detection algorithms for protecting the power grid, based on the knowledge of the expected behavior of the system.
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
The ubiquity of information technologies such as wireless communications, biometric identification systems, online data repositories, and smart electricity grids has created new challenges in information security and privacy. Traditional approaches based on cryptography are often inadequate in such complex systems and fundamentally new techniques must be developed. Information theory provides fundamental limits that can guide the development of methods for addressing these challenges, and the purpose of this book is to introduce the reader to state-of-the-art developments in this field.
As a prototypical example of a system in which such methods can play an important role, one can consider a communication system. In a typical configuration, there is an architectural separation between data encryption and error correction in such systems. The encryption module is based on cryptographic principles and abstracts out the underlying communication channel as an ideal bit-pipe. The error correction module is typically implemented at the physical layer. It adds redundancy into the source message in order to combat channel impairments or multiuser interference and transforms the noisy communication channel into a reliable bit-pipe. While such a separation-based architecture has long been an obvious solution in most systems, a number of applications have emerged in recent years where encryption mechanisms must be aware of the noise structure in the underlying channel, and likewise the error correction and data compression methods must be aware of the associated secrecy constraints required by the application. Such joint approaches can be studied by developing new mathematical models of communication systems that impose both reliability constraints and secrecy constraints. Similar considerations arise throughout the information and communication technologies, and information theoretic approaches can point the way to fundamentally new solutions for such technologies. We refer to this emerging field of research as information theoretic approaches to security and privacy (ITASP). It is notable that this approach leads to guaranteeing information security irrespective of the computational power of the adversary and is a fundamental departure from current computation-based cryptographic solutions. In this book we will highlight among others the following application areas where principles of ITASP have been particularly effective.