Smart Grid Data Aggregation Scheme Based on Local Differential Privacy

Extremely randomised trees machine learning model for electricity theft detection

Data classification of users’ electricity consumption provides an in-depth analysis for users’ electricity consumption status, which plays a vital role in the management and distribution of electric energy. So, some data classification methods have been proposed to solve the classification problem of electricity consumption data. However, plaintext-based data classification may bring about the privacy leakage of electricity consumption data. In this paper, we propose a privacy-preserving classification scheme for electricity consumption data under fog computing-based smart metering system, which is based on convolutional neural network (CNN) model with fully homomorphic method (CKKS). The target of our proposed scheme is to solve the leakage problem of private electricity consumption data during the classification procedure. In our scheme, an improved K-means-based labeling algorithm is constructed to process historical electricity consumption data, which is used as the sample data to train the CNN classification model by cloud server. Also, the fog nodes are only permitted to obtain the related ciphertext parameters of the trained CNN model, and perform the classification of ciphertext-based electricity consumption data generated by fully homomorphic method. Based on the classical testing data, the experimental results show that our proposed classification scheme can provide the high classification accuracy of electricity data while protecting the privacy of electricity data.

Electricity theft is harmful to power grids. Integrating information flows with energy flows, smart grids can help to solve the problem of electricity theft owning to the availability of massive data generated from smart grids. The data analysis on the data of smart grids is helpful in detecting electricity theft because of the abnormal electricity consumption pattern of energy thieves. However, the existing methods have poor detection accuracy of electricity theft since most of them were conducted on one-dimensional (1-D) electricity consumption data and failed to capture the periodicity of electricity consumption. In this paper, we originally propose a novel electricity-theft detection method based on wide and deep convolutional neural networks (CNN) model to address the above concerns. In particular, wide and deep CNN model consists of two components: the wide component and the deep CNN component. The deep CNN component can accurately identify the nonperiodicity of electricity theft and the periodicity of normal electricity usage based on 2-D electricity consumption data. Meanwhile, the wide component can capture the global features of 1-D electricity consumption data. As a result, wide and deep CNN model can achieve the excellent performance in electricity-theft detection. Extensive experiments based on realistic dataset show that wide and deep CNN model outperforms other existing methods.

Certificateless privacy-preserving multidimensional data aggregation scheme with fault-tolerance for fog-based smart grids

A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data

In Smart Grids the Information and Communication Technologies (ICT) could be used to better manage both consumption and production of electricity. The increasing presence of renewable energy sources in production and the permeation of novel consumption types (e.g. Plug-in Hybrid Electric Vehicles (PHEV)) will obviously cause the increase the fluctuation of electrical energy. One possible solution to these problems is development of novel methods for investigating electrical power consumption data series. As the existing learning algorithms of pattern classification are suitable for discovering internal structures of large datasets, it is important to generate a training/testing/validation learning database from existing measurements (e.g. from smart meters), actually via segmentation and labeling by hand. In this paper we propose a novel method for the automatic segmentation with a predefined confidence level. The algorithm is based on the generalized Jensen-Shannon divergence (JSD), and it estimates the change-points (CPTs) in electrical power consumption data. Both the method and some recent results in segmenting one household's power consumption data are presented in this paper.

One of the most common methods to preserve consumers' private data is using secure in-network data aggregation. The security can be provided through the emerging fully (FHE) or partial (PHE) homomorphic encryption techniques. However, an FHE aggregation scheme generates significantly big-size data when compared to traditional encryption methods. The overhead is compounded in hierarchical networks such as Smart Grid Advanced Metering Infrastructure (AMI) as data packets are routed towards the core of the AMI networking infrastructure from the smart meters. In this paper, we first investigate the feasibility and performance of FHE aggregation in AMI networks utilizing the reliable data transport protocol, TCP. Then, we introduce the packet reassembly problem. To address this challenge, we propose a novel packet reassembly mechanism for TCP. We evaluated the effectiveness of our proposed mechanism using both PHE and FHE-based aggregation approaches in AMI in terms throughput and end-to-end delay on an 802.11s-based wireless mesh network by using the ns-3 network simulator. The results indicate significant gains in terms of delay and bandwidth usage with the proposed mechanism.

As a next-generation power system, the smart grid can implement fine-grained smart metering data collection to optimize energy utilization. Smart meters face serious security challenges, such as a trusted third party or a trusted authority being attacked, which leads to the disclosure of user privacy. Blockchain provides a viable solution that can use its key technologies to solve this problem. Blockchain is a new type of decentralized protocol that does not require a trusted third party or a central authority. Therefore, this paper proposes a decentralized privacy-preserving data aggregation (DPPDA) scheme for smart grid based on blockchain. In this scheme, the leader election algorithm is used to select a smart meter in the residential area as a mining node to build a block. The node adopts Paillier cryptosystem algorithm to aggregate the user’s power consumption data. Boneh-Lynn-Shacham short signature and SHA-256 function are applied to ensure the confidentiality and integrity of user data, which is convenient for billing and power regulation. The scheme protects user privacy data while achieving decentralization, without relying on TTP or CA. Security analysis shows that our scheme meets the security and privacy requirements of smart grid data aggregation. The experimental results show that this scheme is more efficient than existing competing schemes in terms of computation and communication overhead.

In smart grid, data aggregation is considered as an essential paradigm in assessing information about current energy usage. To achieve the privacy-preserving goal, several homomorphic-based solutions have been proposed. However, these solutions either consider one-dimensional information or use costly pairing computation in order to ensure source authentication. In fact, smart grid data are likely to be multidimensional (e.g., time, purpose, and so on) for more accurate control. In addition, the aggregation node in smart grid needs to verify data that come from several smart meters in a residential area; hence, the verification must be cost-efficient. In this paper, we propose a scheme that considers multidimensional aggregation with privacy preserving and an efficient verification of smart grid data. The proposal is based on elliptic curve cryptography along with homomorphic encryption and without pairings. The performance analysis shows the efficiency of the scheme for smart grid communications in comparison with existing schemes. For instance, we show that, when an aggregator node is responsible of 600 smart meters, it spends approximately 14 s to verify the data in pairing-based schemes, while only 0.3 s is needed for verification within the proposed scheme.

Pattern-based and context-aware electricity theft detection in smart grid

In recent years, the problem of electrical load forecasting gained attention due to the arrival of new measurement technologies that produce electrical energy consumption data at very short intervals of time. Such short term measurements become voluminous in very short time. The availability of big electrical consumption data allows machine learning techniques to be employed to analyze consumption behavior of every consumer on a greater detail. Predicting the consumption of a residential customer is crucial at this point of time because tailor-made consumer-specific tariffs will play a vital role in load balancing process of Utilities. This paper analyzes the electrical consumption of a single residential customer measured using a smart meter that is capable of measuring electrical consumption at circuit level. The issues and challenges in collecting the data and pre-processing required for making them suitable for data analytics are discussed in detail. A comparison of the performance of different machine learning algorithms implemented using Python’s Scikit-learn module gives an insight on the consumption pattern.

In smart grid, electricity consumption data may be handed over to a third party for various purposes.While government regulations and industry compliance prevent utility companies from improper or illegal sharing of their customers' electricity consumption data, there are some scenarios where it can be very useful.For example, it allows the consumers' data to be shared among various energy resources so the energy resources are able to analyze the data and adjust their operation to the actual power demand.However, it is crucial to protect sensitive electricity consumption data during the sharing process.In this paper, we propose a fine-grained access control scheme (FAC) with efficient attribute revocation and policy updating in smart grid.Specifically, by introducing the concept of Third-party Auditor (TPA), the proposed FAC achieves efficient attribute revocation.Also, we design an efficient policy updating algorithm by outsourcing the computational task to a cloud server.Moreover, we give security analysis and conduct experiments to demonstrate that the FAC is both secure and efficient compared with existing ABE-based approaches.

Smart grid enables two-way communications between smart meters and operation centers to collect real-time power consumption of customers to improve flexibility, reliability, and efficiency of the power system. It brings serious privacy issues to customers, since the meter readings possibly expose customers' activities in the house. Data encryption can protect the readings, but lengthens the data size. Secure data aggregation improves communication efficiency and preserves customers' privacy, while fails to support dynamic billing, or offer integrity protection against public collectors, which may be hacked in reality. In this paper, we define a new security model to formalize the misbehavior of collectors, in which the misbehaving collectors may launch pollution attacks to corrupt power consumption data. Under this model, we propose a novel privacy-preserving smart metering scheme to prevent pollution attacks for the balance of security and efficiency in smart grid. It achieves end-to-end security, data aggregation, and integrity protection against the misbehaving collectors, which act as local gateways to collect and aggregate usage data and forward to operation centers. As a result, the misbehaving collectors cannot access or corrupt power usage data of customers. In addition, we design a dynamic billing mechanism based on individual power consumption maintained on collectors with the verification of customers. Our analysis shows that the proposed scheme achieves secure smart metering and verifiable dynamic billing against misbehaving collectors with low computational and communication overhead.

The enhancement of the intelligent construction of the power grid and widespread popularity of smart meters enable large amounts of electrical energy consumption data to be collected and analyzed. Based on the data, the energy provider gives a guiding price in the future periods to users. It encourages users to be more economical and smarter in the process of using electricity. By applying the social welfare model to equate demand and supply in every time interval, we gain the optimal prices and generation capacity. Nevertheless, the truth is that there is a great gap between the consumers’ booked electrical energy consumption and the optimal generation capacity, causing the power system overload and even outage. This article puts forward a novel automatic process control strategy in order to monitor the gap between the consumers’ booked electrical energy consumption and optimal generation capacity by using statistical method to predict the future one. When the predicted value exceeds the boundary, the energy provider adopts the changeable electricity price to stimulate consumers to adjust their electrical energy demands so that it can have smoothly actual electrical energy consumption. Our adjustment method is data-driven exponential function-based adjustment. Case study results show that the strategy can obtain small adjustment times, stable actual consumption load, and controllable prediction errors. Different from the linear monitoring and adjustment strategy, our approach obtains almost the same adjustment frequency, less standard deviation of residuals, and higher total social welfare and energy provider profit.

As the number of active Internet of Things (IoT) devices increases, managing and securing the enormous amount of data generated by these devices become harder. With their always‐connected smart meters and internet‐enabled electrical appliances, smart grids (SGs) are major data generation sources in IoT. Traditional SGs store and process their collected data in centralized cloud servers. Secure data aggregation (SDA) protocols play an essential role in efficient and private data collection and storage in cloud‐based SGs. Due to a high number of connected devices, centralized cloud‐based SGs may become inadequate to satisfy the processing and storage needs of the collected data. This article introduces two efficient and lightweight SDA protocols for fog computing‐based SGs (FCSG), namely, FCSG‐DF and FCSG‐P, as a complementary solution for processing and storage needs of cloud‐based SGs. By leveraging the distributed nature and other extended capabilities of fog computing, the proposed SDA protocols have a better response time with less computational overhead. Moreover, the privacy of fine‐grained energy consumption data is preserved with the help of the privacy‐preserving data aggregation processes in the hierarchical fog computing structure of FCSG. Extensive performance evaluation results demonstrate that the proposed protocols have superiority in terms of data transmission and storage efficiency compared to cloud‐assisted schemes without data aggregation. Both protocols are analyzed by comparing their success in transmission and storage efficiencies. Moreover, privacy analysis verifies that both protocols effectively ensure the privacy of the data in each layer.