Georgios Kalogridis

Smart Grid Privacy via Anonymization of Smart Metering Data

The security and privacy of future smart grid and smart metering networks is important to their rollout and eventual acceptance by the public: research in this area is ongoing and smart meter users will need to be reassured that their data is secure. This paper describes a method for securely anonymizing frequent (for example, every few minutes) electrical metering data sent by a smart meter. Although such frequent metering data may be required by a utility or electrical energy distribution network for operational reasons, this data may not necessarily need to be attributable to a specific smart meter or consumer. It does, however, need to be securely attributable to a specific location (e.g. a group of houses or apartments) within the electricity distribution network. The method described in this paper provides a 3rd party escrow mechanism for authenticated anonymous meter readings which are difficult to associate with a particular smart meter or customer. This method does not preclude the provision of attributable metering data that is required for other purposes such as billing, account management or marketing research purposes.

Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.

Privacy for Smart Meters: Towards Undetectable Appliance Load Signatures

Smart grid privacy encompasses the privacy of information extracted by analysing smart metering data. In this paper, we suggest that home electrical power routing can be used to moderate the homes load signature in order to hide appliance usage information. In particular, 1) we introduce a power management model using a rechargeable battery, 2) we propose a power mixing algorithm, and 3) we evaluate its protection level by proposing three different privacy metrics: an information theoretic (relative entropy), a clustering classification, and a correlation/regression one; these are tested on different metering datasets. This paper sets the ground for further research on the subject of optimising home energy management with regards to hiding load signatures.

ElecPrivacy: Evaluating the Privacy Protection of Electricity Management Algorithms

The data collected by a home smart meter can potentially reveal sensitive private information about the home resident(s). In this paper, we study how home energy resources can be used to protect the privacy of the collected data. In particular we: a) introduce a power mixing algorithm to selectively protect a set of consumption events; b) develop a range of different privacy protection metrics; c) analyze real smart metering data sampled twice a minute over a period of 13 days; and d) evaluate the protection offered by different power mixing algorithms. Major factors which determine the efficiency of the proposed power mixing algorithms are identified, such as battery capacity and power, and user preferences for privacy-based allocations of battery energy quotas.

Privacy protection system and metrics for hiding electrical events

Smart grid privacy concerns the privacy of information extracted by analysing smart metering data. We present ElecPrivacy: a home electrical power management system that uses a rechargeable battery to mask home energy load signatures and, effectively, protect the privacy of appliance usage information. ElecPrivacy can be studied in the context of the classic communications problem, where input data is passed through a communication channel that distorts it. In this paper, we define and measure how the appearance of ElecPrivacy events can be estimated, or, reversely, how well the secrecy of this data is protected. In particular, we develop a range of privacy metrics by combining clustering, information theoretic (K-divergence), correlation and regression techniques, and testing over a large data set obtained from real home measurements.

Data Mining and Privacy of Personal Behaviour Types in Smart Grid

Privacy protection is one of the key requirements of smart grids. To understand the importance of privacy threats it is necessary to study nature of power signals. In this paper, we propose a well-known statistical method which relies on the empirical probability distribution. The method is used to reveal trends in the power signal data and how these trends are changed if a) different data sampling rates are assumed, and b) a privacy algorithm is applied to protect the power data of different home appliances. Our results suggest that the privacy of personal behaviour types is exposed even if relatively infrequent measurements are obtained. On the other hand, battery-assisted home energy management solutions are more likely to protect the customers.

Smart electric vehicle charging: Security analysis

This paper provides a comprehensive security analysis of the Electric Vehicle (EV) charging service in Smart Grid (SG) environment (i.e. the “smart” EV charging application). It first describes three EV charging scenarios, at home, at work and at public places. Based on these use-case scenarios, the paper presents a model for smart EV charging, consisted of application entities and interactions among them. It then illustrates potential message types communicated among these entities. Based on this model and the exchanged messages, the paper analyses security problems and potential security threats imposed on the entities, which leads to the specification of a set of security and privacy requirements. These requirements could be used to guide the future design of solutions for secure smart EV charging systems and/or a risk/impact assessment of such systems.

Toward Unified Security and Privacy Protection for Smart Meter Networks

The management of security and privacy protection mechanisms is one fundamental issue of future smart grid (SG) and metering networks. Designing effective and economical measures is a nontrivial task due to the following: 1) the large number of system requirements; and 2) the uncertainty over how the system functionalities are going to be specified and to evolve. This paper explores a unified approach for addressing security and privacy of smart metering (SM) systems. In the process, we present a unified framework that entails the analysis and synthesis of security solutions associated with closely interrelated components of a typical SM system. Ultimately, the proposed framework can be used as a guideline for embedding cross-domain security and privacy solutions into SG communication systems.

The power of data: Data analytics for M2M and smart grid

Machine to machine (M2M) communication has been gaining momentum in recent years as a key enabling technology for a wide range of applications including smart grid, e-health, home/industrial automation, and smart cities. However, with the current communication systems mainly optimized for human to human communications, there are important capabilities that need to be developed in M2M systems in order to fully realize the new smart services enabled by M2M. In this paper, we provide an overview of M2M and its applications to smart grid. In particular, we discuss technical areas where data mining and machine learning can play an important role in realizing various smart functionalities in the future power grid. As a case study, we also present a novel phase identification technique in smart grid based on smart meter data. Preliminary results have demonstrated the effectiveness of the proposed algorithm.

Real-time and low cost energy disaggregation of coarse meter data

A novel real-time non-intrusive appliance load monitoring algorithm is introduced. By applying finite state machine training, dynamic appliance disaggregation, rule-based filtering and discrete Fourier analysis of coarse data, several improvements are achieved: 1) reduced appliance model training complexity as compared to existing algorithms, and 2) novel and augmented detection stage. The application of this algorithm on real world data demonstrates that efficiency does not have to be compromised by the relatively lower complexity.