Enabling Fine-grained Access Control with Efficient Attribute Revocation and Policy Updating in Smart Grid

With the development of IoT technology, smart grid has gradually replaced the traditional grid. Smart grid is convenient and fast. It can provide real-time residential electricity monitoring and forecasting, give users better electricity guidance and save a lot of labor costs. Smart meters send customers’ electricity consumption data to the gateway, which aggregates the data and then sends it to the electricity consumption control center. But in this process, there will be a security problem of leakage of customer’s electricity consumption data. Most of the current user data privacy protection collection schemes use homomorphic encryption and randomization techniques. However, some of these schemes require a trusted third-party entity, and some may cause significant computational overhead. Due to the limited computational resources of smart meters, these techniques may be impractical. In this paper, we propose a local differential privacy data aggregation protection scheme based on the idea of grouping perturbation of electricity consumption data according to data domains. Experiments show that our scheme can provide statistical estimates of electricity in the region while satisfying the privacy protection of customers’ electricity consumption data. Moreover, our scheme has small computational and communication overheads, which can meet the application requirements in practical scenarios.

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.

Efficient and fine-grained access control with fully-hidden policies for cloud-enabled IoT

In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset's dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models' performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.

Extremely randomised trees machine learning model for electricity theft detection

Smart grids can establish two-way communication with users, collect their electricity consumption data and provide reasonable pricing, but fine-grained electricity consumption data leads to the leakage of user privacy. In order to protect the privacy of user data and the security of data transmission process, this article proposes an efficient and batch validated privacy protection scheme. In this scheme, legitimate entities transmit encrypted electricity data after mutual authentication. To ensure the correct data is received, we propose a new batch signature verification algorithm. Security analysis shows that our solution achieves user privacy and data security. The simulation experiments provided demonstrate that the efficiency of the proposed scheme can satisfy the requirements of real-time communication.

The Energy industry is facing a set of changes. The old grids need to be replaced, alternative energy market is increasing and consumers want more control of their consumption. On the other hand, the ever-increasing pervasiveness of technology together with the smart paradigm, are becoming the reference point of anyone involved in innovation, and energy management issues. In this context, the information that can potentially be made available by technological innovation is obvious. Nevertheless, in order to turn it into better and more efficient decisions, it is necessary to keep in mind three sets of issues: those related to the management of generated data streams , those related to the quality of the data and finally those related to their usability for human decision-maker. In smart grid , large amounts of and various types of data, such as device status data, electricity consumption data, and user interaction data are collected. Then, as described in several scientific papers, many data analysis techniques, including optimization, forecasting, classification and other, can be applied on the large amounts of smart grid big data . There are several techniques, based on Big Data analysis using computational intelligence techniques, to optimize power generation and operation in real time, to predict electricity demand and electricity consumption and to develop dynamic pricing mechanisms. The aim of the chapter is to critically analyze the way Big Data is utilized in the field of Energy Management in Smart Grid addressing problems and discussing the important trends.

For the problems of non-extensibility of preset access control policy in vehicular ad-hoc networks, considering the high mobility of its topology, this paper proposes an attribute-based and fine-grained access control scheme to protect vehicular resources from invalid access of the malicious node. Firstly, according to the dynamic mobility properties of the vehicles and the road condition, we make a dynamically generated access policy to form the access constraints of the access requestors. Secondly, we propose a trust evaluation method based on the existing scheme. The method defines several trust attributes to calculate the requestor's trust value synthetically. Then the resource provider decides whether to grant the permission or not according to the threshold value. Finally, a dynamic access control scheme for the anonymous vehicles in vehicular ad-hoc networks is established. Results show that our scheme preserves several security requirements, meanwhile, the time computation of policy generation show that our scheme will be applicable for vehicular ad-hoc networks.

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

Fine-grained access control is used to assign unique access privilege to a particular user for accessing real-time data directly from the nodes inside wireless sensor network (WSNs). In this paper, we propose a new fine-grained access control scheme based on key policy attribute based encryption (KP-ABE) suitable for hierarchical WSNs. The protocol provides fine-grained access control with authentication along with efficient user revocation and new node deployment policy. Instead of being quite lightweight in nature, the scheme resists many active and passive security attacks. For formal authentication proof and simulation we use BAN logic and widely accepted AVISPA model checkers respectively.

To monitor and control the states of surrounding infrastructures in smart grid, wireless sensor networks (WSNs) have been perceived to play an important role in connecting diverse smart meters. However, it also incurs many challenges on network security. Firstly, for WSNs, as data are stored and maintained by a large number of sensors deployed in a distributed way, it is significantly hard to ensure distributed data security by using traditional security protection technologies that only can provides guarantees for small scale centralized local networks. Secondly, more fine-grained access control should be provided for smart meters to adapt to the requirements of the multiparty communications between smart grid stakeholders. In this paper, we propose a security-aware distributed service composition scheme for WSNs based smart metering in smart grid based on software defined networks (SDN). Case studies demonstrate the feasibility of proposed scheme. To our best knowledge, this paper is the first to realize software defined security architecture for WSNs based smart metering in smart grid.

With high flexibility and accessibility of data outsourcing environment such as cloud computing environment, several healthcare providers implement electronic personal health records (PHRs) to enable individual patients to manage their own health data in such resilient and scalable environment. However, PHRs contain highly sensitive information of which the security and privacy issues are the critical concern. Besides, PHRs owners should be capable to flexibly and securely define their own access policy for their outsourced data. In addition to the basic authentication feature, existing commercial cloud platforms usually provide symmetric or public key encryption as an optional feature to support data confidentiality for their tenants. However, such traditional encryption schemes are not suitable for data outsourcing environment because of high key management overhead of symmetric encryption and high maintenance cost for handling multiple copies of ciphertext for public key encryption solution. In this paper, we design and develop a secure and fine-grained access control scheme with lightweight access policy update for outsourced PHRs. Our proposed scheme is based on the ciphertext policy attribute-based encryption (CP-ABE) and proxy re-encryption (PRE). In addition, we introduce a policy versioning technique to support the full traceability of policy changes. Finally, we conducted the performance evaluation to demonstrate the efficiency of the proposed scheme.

This paper explores the transformative role of smart grid technology in simplifying contemporary energy management complexities. By integrating advanced communication and automation, smart grids enhance efficiency, reliability, and sustainability in power distribution, paving the way for a more resilient and responsive energy infrastructure Through the integration of cutting-edge communication and automation, smart grids not only bolster efficiency but also elevate the reliability and sustainability of power distribution. This research sheds light on the potential of smart grids to create a more resilient and responsive energy infrastructure, addressing the challenges of our rapidly evolving energy landscape. The exploration extends beyond the surface benefits, delving into the nuanced ways in which smart grids empower energy systems to adapt and evolve. This adaptive prowess not only simplifies the complexities of energy management but also fosters a more resilient and responsive energy infrastructure. The intricate interplay of technological advancements within smart grids forms the bedrock for a paradigm shift in how we conceptualize, distribute, and consume energy resources. As the world grapples with the pressing need for sustainable energy solutions, this research sheds light on the multifaceted contributions of smart grid technology. By paving the way for a harmonized and dynamic energy ecosystem, smart grids stand poised to redefine the future of energy management, offering a promising path towards a more sustainable and resilient global energy landscape. Keywords-Advanced Metering Infrastructure (AMI), Distributed Energy Resources (DERs)

Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity theft detectors, etc. These issues lead the classification techniques to low accuracy, minimum precision, low F1 score, and overfitting problems. For these reasons, it is extremely crucial to design such a novel strategy that is capable to tackle these issues and yield outstanding ETD performance. In this article, electricity theft happening in SGs is detected using a novel ETD approach. The proposed approach comprises recursive feature elimination (RFE), k nearest neighbor oversampling (KNNOR), bidirectional long short term memory (BiLSTM), and logit boosting (LogitBoost) techniques. Furthermore, three BiLSTM networks and a LogitBoost model are combined to make a BiLSTM-LogitBoost stacking ensemble model. Data preprocessing and feature selection followed by data balancing and electricity theft classification are the four major stages of the model proposed for ETD. It is obvious from the simulations performed using state grid corporation of China (SGCC)’s electricity consumption (EC) data that our proposed model achieves 96.32% precision, 94.33% F1 score, and 89.45% accuracy, which are higher than all the benchmarks employed in this study.

Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. This may be due to physical limitations of the transmission networks, or due to consumers limiting frequent transmission of data from sensors located at their premises for security and privacy concerns. Such scenarios lead to partial data problem and raise the issue of data veracity in big data applications. We describe a novel solution to the problem of making short term predictions (up to a few hours ahead) in absence of real-time data from sensors in Smart Grid. A key implication of our work is that by using real-time data from only a small subset of influential sensors, we are able to make predictions for all sensors. We thus reduce the communication complexity involved in transmitting sensory data in Smart Grids. We use real-world electricity consumption data from smart meters to empirically demonstrate the usefulness of our method. Our dataset consists of data collected at 15-min intervals from 170 smart meters in the USC Microgrid for 7 years, totaling 41,697,600 data points.