Privacy-Friendly Load Scheduling of Deferrable and Interruptible Domestic Appliances in Smart Grids

The massive integration of renewable energy sources in the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable and/or interruptible electrical appliances. However, communicating the users' needs about the usage of their appliances also leaks sensitive information about their habits and lifestyles, thus arising privacy concerns. This paper proposes a framework to allow the coordination of energy consumption without compromising the privacy of the users: the service requests generated by the domestic appliances are divided into crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol by a set of schedulers, which schedule the requests by directly operating on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained by means of an Integer Linear Programming formulation

Privacy-friendly appliance load scheduling in smart grids

Abstract—The massive integration of renewable energy sources into the power grid ecosystem with the aim of reducing carbon emissions must cope with their intrinsically intermittent and unpredictable nature. Therefore, the grid must improve its capability of controlling the energy demand by adapting the power consumption curve to match the trend of green energy generation. This could be done by scheduling the activities of deferrable electrical appliances. However, communicating the users ’ needs about the usage of the electrical appliances leaks sensitive information about habits and lifestyles of the customers, thus arising privacy concerns. This paper proposes a privacy-preserving framework to allow the coordination of energy con-sumption without compromising the privacy of the users: the ser-vice requests generated by the domestic appliances are diveded in crypto-shares using Shamir Secret Sharing scheme and collected through an anonymous routing protocol based on Crowds by a set of schedulers, which schedule the requests operating directly on the shares. We discuss the security guarantees provided by our proposed infrastructure and evaluate its performance, comparing it with the optimal scheduling obtained through an Integer Linear Programming formulation. I

Evaluation of the Precision-Privacy Tradeoff of Data Perturbation for Smart Metering

Abstract: Smart grid users and standardization committees require that utilities and third parties collecting metering data employ techniques for limiting the level of precision of the gathered household measurements to a granularity no finer than what is required for providing the expected service. Data aggregation and data perturbation are two such techniques. This paper provides quantitative means to identify a tradeoff between the aggregation set size, the precision on the aggregated measurements, and the privacy level. This is achieved by formally defining an attack to the privacy of an individual user and calculating how much its success probability is reduced by applying data perturbation. Under the assumption of time-correlation of the measurements, colored noise can be used to even further reduce the success probability. The tightness of the analytical results is evaluated by comparing them to experimental data

Enabling privacy in a gaming framework for smart electricity and water grids

Serious games are potentially powerful tools to influence users' preferences and attitudes. However, privacy concerns related to the misuse of data gathered from the players may emerge in online-gaming interactions. This work proposes a privacy-friendly framework for a gaming platform aimed at reducing energy and water usage, where players are grouped in teams with the challenge of maintaining the aggregated consumption of its members below a given threshold. We discuss a communication protocol which enables the team members to compute their overall consumption with- out disclosing individual measurements. Moreover, the protocol prevents the gaming platform from learning the consumption data and challenge objectives of the players. Correctness and truthfulness checks are included in the protocol to detect cheaters declaring false consumption data or providing altered game results. The security and performance of the framework are assessed, showing that scalability is ensured thanks to the limited data exchange and lightweight cryptographic operations

On security and privacy of consensus-based protocols in blockchain and smart grid

In recent times, distributed consensus protocols have received widespread attention in the area of blockchain and smart grid. Consensus algorithms aim to solve an agreement problem among a set of nodes in a distributed environment. Participants in a blockchain use consensus algorithms to agree on data blocks containing an ordered set of transactions. Similarly, agents in the smart grid employ consensus to agree on specific values (e.g., energy output, market-clearing price, control parameters) in distributed energy management protocols. This thesis focuses on the security and privacy aspects of a few popular consensus-based protocols in blockchain and smart grid. In the blockchain area, we analyze the consensus protocol of one of the most popular payment systems: Ripple. We show how the parameters chosen by the Ripple designers do not prevent the occurrence of forks in the system. Furthermore, we provide the conditions to prevent any fork in the Ripple network. In the smart grid area, we discuss the privacy issues in the Economic Dispatch (ED) optimization problem and some of its recent solutions using distributed consensus-based approaches. We analyze two state of the art consensus-based ED protocols from Yang et al. (2013) and Binetti et al. (2014). We show how these protocols leak private information about the participants. We propose privacy-preserving versions of these consensus-based ED protocols. In some cases, we also improve upon the communication cost

Data processing of high-rate low-voltage distribution grid recordings for smart grid monitoring and analysis

Power networks will change from a rigid hierarchic architecture to dynamic interconnected smart grids. In traditional power grids, the frequency is the controlled quantity to maintain supply and load power balance. Thereby, high rotating mass inertia ensures for stability. In the future, system stability will have to rely more on real-time measurements and sophisticated control, especially when integrating fluctuating renewable power sources or high-load consumers like electrical vehicles to the low-voltage distribution grid. In the present contribution, we describe a data processing network for the in-house developed low-voltage, high-rate measurement devices called electrical data recorder (EDR). These capture units are capable of sending the full high-rate acquisition data for permanent storage in a large-scale database. The EDR network is specifically designed to serve for reliable and secured transport of large data, live performance monitoring, and deep data mining. We integrate dedicated different interfaces for statistical evaluation, big data queries, comparative analysis, and data integrity tests in order to provide a wide range of useful post-processing methods for smart grid analysis. We implemented the developed EDR network architecture for high-rate measurement data processing and management at different locations in the power grid of our Institute. The system runs stable and successfully collects data since several years. The results of the implemented evaluation functionalities show the feasibility of the implemented methods for signal processing, in view of enhanced smart grid operation. © 2015, Maaß et al.; licensee Springer

Privacy-preserving power usage control in smart grids

The smart grid (SG) has been emerging as the next-generation intelligent power grid system because of its ability to efficiently monitor, predicate, and control energy generation, transmission, and consumption by analyzing users\u27 real-time electricity information. Consider a situation in which the utility company would like to smartly protect against a power outage. To do so, the company can determine a threshold for a neighborhood. Whenever the total power usage from the neighborhood exceeds the threshold, some or all of the households need to reduce their energy consumption to avoid the possibility of a power outage. This problem is referred to as threshold-based power usage control (TPUC) in the literature. In order to solve the TPUC problem, the utility company is required to periodically collect the power usage data of households. However, it has been well documented that these power usage data can reveal consumers\u27 daily activities and violate personal privacy. To avoid the privacy concerns, privacy-preserving power usage control (P-PUC) protocols are proposed under two strategies: adjustment based on maximum power usage and adjustment based on individual power usage. These protocols allow a utility company to manage power consumption effectively and at the same time, preserve the privacy of all involved parties. Furthermore, the practical value of the proposed protocols is empirically shown through various experiments --Abstract, page iii