Privacy-Preserving Data Aggregation for Single Point of Failure in Smart Grid

Fog-enabled secure multiparty computation based aggregation scheme in smart grid

Efficient power management in smart grid relies on collecting fine-grained power consumption data from users. However, these data may reveal sensitive information about individuals' habits and lifestyles. Various multidimensional data aggregation schemes leveraging public key encryption (PKE) algorithms have been proposed to address this problem. Never theless, most of these schemes come with significant performance costs. In addition, if the secret key of a smart meter was leaked, the confidentiality of encrypted user power data could be at risk. In this article, we propose a lightweight, multidimensional, and privacy-preserving data aggregation scheme with fault-tolerance and key-leakage resilience for smart grid without relying on a trusted third party (TTP), named FKLM-PDA, in which a novel data packaging method that transforms users' multidimensional data into a one-dimensional format is designed, enabling data center parse aggregated results in each dimension, reducing computation and communication costs. For better efficiency, an effective encryption algorithm is proposed to replace the expensive additive homomorphic PKE, like the Paillier cryptosystem, which combines a random masking with secret-sharing based key separation, ensuring threshold key-leakage resilience under a bounded, non-colluding leakage model. Furthermore, not only does FKLM-PDA enhance the fault tolerance mechanism of data transmission from smart meters to a corresponding fog node, but also it supports dynamic user management for joining and exiting improving scalability. Security analysis confirms that FKLM-PDA is privacy-preserving and secure while guaranteeing key-leakage resilience, fault tolerance, authentication, and data integrity. Through performance evaluations, FKLM-PDA outper forms the existing schemes and is superior in computation and functional in communication.

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.

Due to the renewable and natural energy resources, there exist many opportunities to use smart grids and smart meters that enhance energy efficiency. With the increasing supply and requirements of energy, many smart grid models have been proposed in the literature. One of the most popular models is to use the centralized energy sharing model. However, the centralized energy sharing model is vulnerable to reliability issues such as a single point of failure. Due to the centralized energy sharing model, maintaining trust and reliability becomes a challenging issue. In this article, we propose a blockchain-based energy trading model to solve the issues energy sharing model, namely, a single point of failure, load balancing, and price governance. The proposed model improves the trust and reliability of the energy-sharing system. The integration of blockchain with the smart grid opens up numerous possibilities in the area of energy sharing and enhances privacy and security.

In the TBFC (Tree-Based Fog Computing) and TWTBFC (Two-Way TBFC) models the electric energy consumed by fog nodes and servers can be reduced in the fog computing (FC) model. Here, fog nodes are hierarchically structured in a height-balanced tree, where a root node is a cloud of servers, leaf nodes are edge nodes which communicate with devices, and each node receives data from child nodes and sends the processed data to a parent node. In the TWTBFC model, nodes send processed data to not only a parent node but also each child node. In order to reduce the network traffic in the TWTBFC model, only aggregate nodes at some level collect the output data of every other aggregate node, i.e. aggregate data. Since only target actuators are to be activated, the aggregate data has to be only delivered to target actuators. Nodes whose descendant actuators are target ones are relay nodes. On receipt of aggregate data, only relay nodes forward the aggregate data to the child nodes. We evaluate the new TWTBFC model in terms of energy consumption of nodes and number of messages transmitted to deliver aggregate data to edge nodes.

Reducing the power supply-demand gap and increasing reliability of power supply are the challenges of current energy management. Implementation of smart grid, smart meters and smart metering can be a possible solution for power demand reduction, efficient power supply management, and optimization of management resource usages. Smart meters include sophisticated measurement and calculation hardware, software, calibration and communication capabilities. For interoperability within a smart grid infrastructure, smart meters are designed to perform functions, and store and communicate data according to certain standards. In this work we discuss smart meter and various elements of smart metering, current state of the technologies related to smart grid, smart meter, advanced metering infrastructure (AMI), and meter data flow in smart grid. We also discuss standards related to smart meter, meter data format and data transmission, functions of smart meter, and functionalities of smart meters, currently deployed by utilities around the world.

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.

In the fog computing (FC) models, a fog node supports application processes to calculate output data on input data from sensors and other fog nodes and sends the output data to target fog nodes which can calculate on the output data. In this paper, we consider the MPSFC (Mobile topic-based PS (publish/subscribe) FC) model where mobile fog nodes communicate with one another by publishing and subscribing messages in wireless networks. Subscription topics of a fog node denote input data on which the fog node can calculate and publication topics of a message show data carried by the message. A fog node only receives a message whose publication topics shares a common topic with the subscription topics. In the TBDT protocol proposed in our previous studies, a fog node only publishes a message of the output data to a target fog node in the communication range. Here, while a fewer number of messages are transmitted, the delivery ratio of messages in the TBDT protocol is smaller than the epidemic routing protocol. In this paper, we propose an ETBDT (Epidemic and Topic-Based Data Transmission) protocol in order to increase the delivery ratio, where mobile fog nodes forward messages to not only target nodes but also non-target nodes in the communication range. In the evaluation, we show the delivery ratio in the ETBDT protocol is larger than the TBTD protocol.

With advances in fog and edge computing, various problems such as data processing for large Internet of Things (IoT) systems can be solved in an efficient manner. One such problem for the next generation smart grid (SG) IoT system comprising of millions of smart devices is the data aggregation problem. Traditional data aggregation schemes for SGs incur high computation and communication costs, and in recent years, there have been efforts to leverage fog computing with SGs to overcome these limitations. In this article, a new fog-enabled privacy-preserving data aggregation scheme (FESDA) is proposed. Unlike existing schemes, the proposed scheme is resilient to false data injection attacks by filtering out the inserted values from external attackers. To achieve privacy, a modified version of the Paillier cryptosystem is used to encrypt the consumption data of the smart meter (SM) users. In addition, FESDA is fault-tolerant, which means, the collection of data from other devices will not be affected even if some of the SMs malfunction. We evaluate its performance along with three other competing schemes in terms of aggregation, decryption, and communication costs. The findings demonstrate that FESDA reduces the communication cost by 50%, when compared with the privacy-preserving fog-enabled data aggregation scheme.

An efficient dependable smart power grid relies on the secure real-time data collection and transmission service provided by a monitoring system. In such a system, the measuring units, such as phasor measurement units (PMUs) and smart meters (SMs), are critical. These measuring equipments function as sensors in the smart grid. Data exchanges between these sensors and the central controller are protected by various security protocols. These protocols usually contain computationally intensive cryptographic algorithms that cause heavy energy overhead to the sensor nodes. Since PMUs and SMs are mostly energy-constrained, the problem of how to ensure the secure communication with minimum energy cost becomes a critical issue for the functionality of the whole smart grid. In this article, we focus on the low power secure communication of the PMUs and SMs. We take two wireless sensor platforms as examples to experimentally investigate the approaches and principles of reconciling the two conflicting system requirements-communication security and low energy consumptions. The proposed methods are general ones and applicable to other energy-constrained yet security sensitive systems.

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

Advanced Metering Infrastructure (AMI) facilitates the communication between smart meters and network operators in smart grid. For better demand-response management, smart meters are supposed to send live or sometimes periodic consumption reports. If such reports are intercepted or eavesdropped by a malicious entity, customers’ privacy is compromised, since vital information can be inferred from power consumption data. In this article, we propose a novel homomorphic privacy-preserving protocol (called NHP <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ) for data aggregation that has a low computational cost compared to its rivals. It is fault-tolerant, supports multi-category aggregation, and can do batch verification at the intermediate aggregator as well as the central system. The proposed protocol is secure even when the gateway or aggregator turns malicious. It does not allow any compromised meter to find other users’ consumption information either. Moreover, in this scheme, the central system cannot infer any usage data even if it is curious and gains access to the data packets sent from meters to the intermediate aggregator. A comprehensive and comparative analysis is carried out at the end of this article which shows the advantages of the proposed scheme in terms of security features and cost.

In smart grids, smart meters periodically collect users' fine-grained multi-dimensional energy data, which poses great concerns on users' privacy and security. Existing privacy preserving multi-dimensional aggregation schemes suffer from heavy computational burdens, especially for smart meters with limited computational resources. To address these limitations, in this paper we propose an efficient and flexible multi-dimensional data aggregation scheme called MDA-SMuSha, by which smart meters employ the Shamir's multi-secret sharing to generate a set of shared secrets, with the first one kept locally, while the remained ones are packaged and then uploaded to a control center via an aggregator. By the MDA-SMuSha scheme, aggregation results of smart meters' multi-dimensional energy data during multiple periods can be obtained, with only one time of Paillier encryption conducted on the smart meters. In addition, it allows the control center to send query requests flexibly, i.e., at a pre-specified frequency or whenever it wants to obtain statistical data of interests. Rigorous security analyses show that the MDA-SMuSha scheme satisfies security requirements of privacy-preservation, authenticity and data integrity as well as fault-tolerance. Both theoretical analyses and experiment results show that the MDA-SMuSha scheme outperforms state-of-the art methods in terms of computation costs, with comparable communication costs.

Smart grid (SG) is an efficient electrical grid that provides opportunities to manage the energy load in a reliable and efficient way. Moreover, smart meters (SMs) are introduced, which play a vital role in communication between homes and SG. SMs manage and monitor the energy consumption of homes. SGs and SMs produce a big data, which is very hard to store and process even with cloud computing. For this purpose fog computing concept is introduced, which provides a good environment for computing and storing the data of SGs and SMs before transmitting them to cloud. The concept of fog computing, acts as a bridge between cloud and SGs. In this paper, a cloud-fog based architecture is integrated with SG for efficient energy management of buildings with SMs. To manage the energy requirements of consumers, micro grids (MGs) are available near to the buildings. Fogs are distributed over the world, overhaul the cloud via important features, including low latency and increased security for MGs. To balance the load on cloud and fogs, three load balancing algorithms are used. These algorithms are round robin (RR), throttled and greedy. Closest datacenter policy is used to compare their results. Greedy gives better results than RR and throttled.

Fog computing (also known as edge computing) is a decentralized computing architecture that seeks to minimize service latency and average response time in IoT applications by providing compute and network services physically close to end-users. Fog environment consists of a network of fog nodes and IoT applications are composed of containerized microservices communicating with each other. Due to limited resources of fog nodes, it is often not possible to deploy all the containers of an application on a single fog node. Therefore, communicating containers need to be distributed on multiple fog nodes. Distribution and management of containerized IoT applications is always a critical issue to the system performance in a fog environment. Kubernetes, an open-source system, has grown into a container orchestration standard by simplifying the deployment and management of containerized applications. Despite the progress made by the academia and industry with respect to container management and the wide-scale acceptance of Kubernetes in cloud environments, container management in fog environment is still in the early stage in terms of research and practical deployment. This article aims to fill this gap by analyzing the expediency of Kubernetes container orchestration tool in the fog computing model. The paper also highlights limitations with the current Kubernetes approach and provide ideas for further research to adapt to the needs of the fog environment. Lastly, we provide experiments that demonstrate the feasibility and industrial practicality of deploying and managing containerized IoT applications in the fog computing environment.