Privacy Preservation in Smart Grid Environment

2019 ◽  
pp. 158-182
Author(s):  
Muhammad Aminu Lawal ◽  
Syed Raheel Hassan
Keyword(s):  
Energy Consumption ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Privacy Preservation ◽  
User Privacy ◽  
Privacy And Security ◽  
Electricity Grid ◽  
Fine Grained ◽  
Comparison Table

Smart grids are conceived to ensure smarter generation, transmission, distribution, and consumption of electricity. It integrates the traditional electricity grid with information and communication technology. This enables a two-way communication among the smart grid entities, which translates to exchange of information about fine-grained user energy consumption between the smart grid entities. However, the flow of user energy consumption data may lead to the violation of user privacy. Inference on such data can expose the daily habits and types of appliances of users. Thus, several privacy preservation schemes have been proposed in the literature to ensure the privacy and security requirements of smart grid users. This chapter provides a review of some privacy preservation schemes. The schemes operational procedure, strengths, and weaknesses are discussed. A taxonomy, comparison table, and comparative analysis are also presented. The comparative analysis gives an insight on open research issues in privacy preservation schemes.

Privacy Preservation in Smart Grid Environment

2021 ◽  
pp. 1392-1410
Author(s):  
Muhammad Aminu Lawal ◽  
Syed Raheel Hassan
Keyword(s):  
Energy Consumption ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Privacy Preservation ◽  
User Privacy ◽  
Privacy And Security ◽  
Electricity Grid ◽  
Fine Grained ◽  
Comparison Table

Smart grids are conceived to ensure smarter generation, transmission, distribution, and consumption of electricity. It integrates the traditional electricity grid with information and communication technology. This enables a two-way communication among the smart grid entities, which translates to exchange of information about fine-grained user energy consumption between the smart grid entities. However, the flow of user energy consumption data may lead to the violation of user privacy. Inference on such data can expose the daily habits and types of appliances of users. Thus, several privacy preservation schemes have been proposed in the literature to ensure the privacy and security requirements of smart grid users. This chapter provides a review of some privacy preservation schemes. The schemes operational procedure, strengths, and weaknesses are discussed. A taxonomy, comparison table, and comparative analysis are also presented. The comparative analysis gives an insight on open research issues in privacy preservation schemes.

scholarly journals Preserving User Privacy and Security in Context-Aware Mobile Platforms

2019 ◽  
pp. 1203-1230 ◽  
Author(s):  
Prajit Kumar Das ◽  
Dibyajyoti Ghosh ◽  
Pramod Jagtap ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Context Aware ◽  
Context Modeling ◽  
User Privacy ◽  
Mobile Platforms ◽  
Privacy And Security ◽  
Semantic Web Technologies ◽  
Fine Grained ◽  
New Class ◽  
Context Data ◽  
Collaborative Context

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

scholarly journals MACHINE LEARNING AND IOT FOR SMART GRID

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 233-240
Author(s):  
M. Fouad ◽  
R. Mali ◽  
A. Lmouatassime ◽  
M. Bousmah
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Smart Grid ◽  
Power Distribution ◽  
Short Term Memory ◽  
Electrical Energy ◽  
Distribution Networks ◽  
Smart Meters ◽  
Power Distribution Networks ◽  
Electricity Grid

Abstract. The current electricity grid is no longer an efficient solution due to increasing user demand for electricity, old infrastructure and reliability issues requires a transformation to a better grid which is called Smart Grid (SG). Also, sensor networks and Internet of Things (IoT) have facilitated the evolution of traditional electric power distribution networks to new SG, these networks are a modern electricity grid infrastructure with increased efficiency and reliability with automated control, high power converters, modern communication infrastructure, sensing and measurement technologies and modern energy management techniques based on optimization of demand, energy and availability network. With all these elements, harnessing the science of Artificial Intelligence (AI) and Machine Learning (ML) methods become better used than before for prediction of energy consumption. In this work we present the SG with their architecture, the IoT with the component architecture and the Smart Meters (SM) which play a relevant role for the collection of information of electrical energy in real time, then we treat the most widely used ML methods for predicting electrical energy in buildings. Then we clarify the relationship and interaction between the different SG, IoT and ML elements through the design of a simple to understand model, composed of layers that are grouped into entities interacting with links. In this article we calculate a case of prediction of the electrical energy consumption of a real Dataset with the two methods Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), given their precision performances.

scholarly journals Preserving User Privacy and Security in Context-Aware Mobile Platforms

2017 ◽  
pp. 166-193
Author(s):  
Prajit Kumar Das ◽  
Dibyajyoti Ghosh ◽  
Pramod Jagtap ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Context Aware ◽  
Context Modeling ◽  
User Privacy ◽  
Mobile Platforms ◽  
Privacy And Security ◽  
Semantic Web Technologies ◽  
Fine Grained ◽  
New Class ◽  
Context Data ◽  
Collaborative Context

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

scholarly journals A New Secure and Anonymous Metering Scheme for Smart Grid Communications

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4751 ◽  
Author(s):  
Shaohao Xie ◽  
Fangguo Zhang ◽  
Huizhi Lin ◽  
Yangtong Tian
Keyword(s):  
Smart Grid ◽  
Security Requirements ◽  
Smart Meter ◽  
Privacy And Security ◽  
Trusted Platform Module ◽  
Fine Grained ◽  
Smart Grid Communications ◽  
Security Issues ◽  
Trusted Platform ◽  
Smart Grid Communication

The smart meter is one of the most important components of the smart grid, which enables bi-directional communication between electric power providers and in-home appliances. However, the fine-grained metering mechanism that reports real-time electricity usage to the provider may result in some privacy and security issues for the owner of the smart meter. In this paper, we propose a new secure and anonymous smart metering scheme based on the technique of direct anonymous attestation and identity-based signatures. We utilize the trusted platform module to realize the tamper resistance of the smart meter. Moreover, our scheme is able to detect malfunctioning meters in which data is reported more than once in a time period. Finally, the performance and security results show that our proposed scheme is efficient and satisfies the security requirements of the smart grid communication system.

Comparative Analysis of Load-Shaping-Based Privacy Preservation Strategies in a Smart Grid

2017 ◽  
Vol 13 (6) ◽  
pp. 3226-3235 ◽  
Author(s):  
Cihan Emre Kement ◽  
Hakan Gultekin ◽  
Bulent Tavli ◽  
Tolga Girici ◽  
Suleyman Uludag
Keyword(s):  
Comparative Analysis ◽  
Smart Grid ◽  
Privacy Preservation

scholarly journals A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

2021 ◽  
Author(s):  
ANKIT GHOSH ◽  
ALOK KOLE
Keyword(s):  
Machine Learning ◽  
Comparative Analysis ◽  
Smart Grid ◽  
Smart Grids ◽  
Machine Learning Algorithms ◽  
Electricity Sector ◽  
Stochastic Gradient Descent ◽  
Energy Output ◽  
Gradient Boosting ◽  
Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

scholarly journals Blockchain in Smart Grids: A Review on Different Use Cases

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4862 ◽  
Author(s):  
Tejasvi Alladi ◽  
Vinay Chamola ◽  
Joel J. P. C. Rodrigues ◽  
Sergei A. Kozlov
Keyword(s):  
Smart Grid ◽  
Smart Grids ◽  
Distribution Systems ◽  
Security And Privacy ◽  
Privacy And Security ◽  
Energy Trading ◽  
Blockchain Technology ◽  
Efficient Data ◽  
Privacy Issues ◽  
The Internet Of Things

With the integration of Wireless Sensor Networks and the Internet of Things, the smart grid is being projected as a solution for the challenges regarding electricity supply in the future. However, security and privacy issues in the consumption and trading of electricity data pose serious challenges in the adoption of the smart grid. To address these challenges, blockchain technology is being researched for applicability in the smart grid. In this paper, important application areas of blockchain in the smart grid are discussed. One use case of each area is discussed in detail, suggesting a suitable blockchain architecture, a sample block structure and the potential blockchain technicalities employed in it. The blockchain can be used for peer-to-peer energy trading, where a credit-based payment scheme can enhance the energy trading process. Efficient data aggregation schemes based on the blockchain technology can be used to overcome the challenges related to privacy and security in the grid. Energy distribution systems can also use blockchain to remotely control energy flow to a particular area by monitoring the usage statistics of that area. Further, blockchain-based frameworks can also help in the diagnosis and maintenance of smart grid equipment. We also discuss several commercial implementations of blockchain in the smart grid. Finally, various challenges to be addressed for integrating these two technologies are discussed.

scholarly journals Preserving User Privacy and Security in Context-Aware Mobile Platforms

2019 ◽  
pp. 408-434
Author(s):  
Prajit Kumar Das ◽  
Dibyajyoti Ghosh ◽  
Pramod Jagtap ◽  
Anupam Joshi ◽  
Tim Finin
Keyword(s):  
Context Aware ◽  
Context Modeling ◽  
User Privacy ◽  
Mobile Platforms ◽  
Privacy And Security ◽  
Semantic Web Technologies ◽  
Fine Grained ◽  
New Class ◽  
Context Data ◽  
Collaborative Context

Contemporary smartphones are capable of generating and transmitting large amounts of data about their users. Recent advances in collaborative context modeling combined with a lack of adequate permission model for handling dynamic context sharing on mobile platforms have led to the emergence of a new class of mobile applications that can access and share embedded sensor and context data. Most of the time such data is used for providing tailored services to the user but it can lead to serious breaches of privacy. We use Semantic Web technologies to create a rich notion of context. We also discuss challenges for context aware mobile platforms and present approaches to manage data flow on these devices using semantically rich fine-grained context-based policies that allow users to define their privacy and security need using tools we provide.

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