An efficient data aggregation scheme for privacy-friendly dynamic pricing-based billing and demand-response management in smart grids

Smart grids take advantage of information and communication technologies to achieve energy efficiency, automation and reliability. These systems allow two-way communications and power flow between the grid and consumers. However, these bidirectional communications introduce several security and privacy threats to consumers. One of the open challenges in this context is user privacy when smart meters are used to capture fine-grained energy usage information. Although considerable research has been carried out in this direction, most of the existing solutions invariably introduce computational complexity and overhead, which makes them infeasible for resource constrained smart meters. In this paper, we propose a privacy-friendly and efficient data aggregation scheme (EDAS) for dynamic pricing based billing and demand-response management in smart grids. To the best of our knowledge, this is the first paper to address privacy in the context of billing under dynamic electricity pricing. Security and performance analyses show that the proposed scheme offers better privacy protection for electric meter reading aggregation and computational efficiency, as compared to existing schemes

Correlated Differential Privacy: Feature Selection in Machine Learning

© 2005-2012 IEEE. Privacy preserving in machine learning is a crucial issue in industry informatics since data used for training in industries usually contain sensitive information. Existing differentially private machine learning algorithms have not considered the impact of data correlation, which may lead to more privacy leakage than expected in industrial applications. For example, data collected for traffic monitoring may contain some correlated records due to temporal correlation or user correlation. To fill this gap, in this article, we propose a correlation reduction scheme with differentially private feature selection considering the issue of privacy loss when data have correlation in machine learning tasks. The proposed scheme involves five steps with the goal of managing the extent of data correlation, preserving the privacy, and supporting accuracy in the prediction results. In this way, the impact of data correlation is relieved with the proposed feature selection scheme, and moreover the privacy issue of data correlation in learning is guaranteed. The proposed method can be widely used in machine learning algorithms, which provide services in industrial areas. Experiments show that the proposed scheme can produce better prediction results with machine learning tasks and fewer mean square errors for data queries compared to existing schemes

A Critical Overview of Privacy-Preserving Approaches for Collaborative Forecasting

Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. The paper also provides mathematical proofs and numerical analysis to evaluate existing privacy-preserving methods, dividing them into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while the original data in iterative model fitting processes, in which intermediate results are shared, can be inferred after some iterations

Game Theory Based Privacy Protection for Context-Aware Services

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information

Generating Energy Data for Machine Learning with Recurrent Generative Adversarial Networks

The smart grid employs computing and communication technologies to embed intelligence into the power grid and, consequently, make the grid more efficient. Machine learning (ML) has been applied for tasks that are important for smart grid operation including energy consumption and generation forecasting, anomaly detection, and state estimation. These ML solutions commonly require sufficient historical data; however, this data is often not readily available because of reasons such as data collection costs and concerns regarding security and privacy. This paper introduces a recurrent generative adversarial network (R-GAN) for generating realistic energy consumption data by learning from real data. Generativea adversarial networks (GANs) have been mostly used for image tasks (e.g., image generation, super-resolution), but here they are used with time series data. Convolutional neural networks (CNNs) from image GANs are replaced with recurrent neural networks (RNNs) because of RNN’s ability to capture temporal dependencies. To improve training stability and increase quality of generated data,Wasserstein GANs (WGANs) and Metropolis-Hastings GAN (MH-GAN) approaches were applied. The accuracy is further improved by adding features created with ARIMA and Fourier transform. Experiments demonstrate that data generated by R-GAN can be used for training energy forecasting models

Data Analytics for Privacy in Smart Grids

The emergence of smart grids has allowed for integrating new technologies in the power grid, with information flowing across the system allowing for more efficient power delivery and event response. Demand response is a new technology enabled by smart grids, which is a program aiming to reduce or shift peak demand by varying the price of electricity or offering incentives for changing consumption habits.Despite demand response benefits, privacy advocates have raised concerns with information leakages allowed by the type of high-resolution data collected by smart meters, as it can reveal customer usage patterns and different parties can take advantage of that data. In this thesis, a utility vs. privacy framework is developed to maximize the utility of using smart meter data while also minimizing the privacy leakages from the smart meter.Two frameworks are developed, the first, a fault localization technique for radial distribution systems by using alarm processing through binary integer linear programming. The second, a power scheduling tool that uses renewables, a battery, and appliance scheduling to disguise the customer usage patterns by matching it to an average and the resulting collected data is not revealing of any characteristics the customer wants to hide.Fault localization was tested on two radial distribution systems, and locates the fault every time, with the variation in time till detection depending on system size, how the system is branched, fault location, and sampling rate. Power scheduling was tested using simulated home data, different scenarios are run by varying battery, solar, appliance, and privacy parameters, and results are compared for various sampling rates. Both frameworks were successful in hiding privacy leakages based their respective privacy metric.Future research on the fault localization could expand to find two faults simultaneously, along with implementing an emergency mode to find faults quicker in a sampling cycle. The power scheduling framework could expand to include thermostatically controlled load scheduling, by implementing deep learning algorithms on each home and factoring in variables such as historic data of weather, time of day, and day of week to determine how thermostatically controlled loads could fit into the scheduling problem