Security and privacy preserving data aggregation in cloud computing

Advanced smart meters generate meter readings in a time unit less than a second. Fine-grained meter readings enable various smart grid applications, such as load monitoring, automatic billing, and power generation planning. However, those meter readings threaten individuals' privacy by revealing details of one's daily activities. The time granularity of smart meters is often much finer than the one a smart grid application demands. Thus, the storage and access control mechanisms of meter readings are critical to balancing privacy requirements and application functionalities. Previous studies address the issue by considering a locally trusted storage device and using cryptographic primitives. We consider a storage outsourcing scenario, where the external storage environment is semi-trusted. We construct a privacy preserving metering system by using a trusted platform module in a smart meter and pseudorandom number generators inside the module. Our system guarantees the secure storage of meter readings and supports multiple time granularities. In our system, a user grants a service provider an access right over meter readings at a time granularity S. The granted service provider is only allowed to get the power consumption at a time unit of the granted time granularity. Our system provides a simple yet very practical solution to the privacy preserving smart metering system. Moreover, we provide a privacy model to capture the privacy requirement and show that our system is privacy preserving against honest-but-curious service providers.

Smart metering systems are electronic systems that measure the electricity consumption, ensuring a secure two-way transmission of information to the end customer and providing more information than a conventional meter, using electronic communication. This type of smart metering technologies has been implemented in three pilot projects from a Romanian region, in order to ensure the collection, analysis and processing of electricity specific data, as well as the connection/disconnection of each consumer. The implementation of smart meters in the three pilot areas led to a more accurate recording of final electricity consumption and a detailed monitoring of the electricity consumption by types of consumption profiles, as well as the electricity technical power losses (ETL) indicator. This paper aims to analyze the evolution of electricity technical losses registered by smart metering systems in each of the three pilot projects and also for the whole region in a certain period, in order to demonstrate the need and importance of monitoring this type of power losses and to find the best methods to reduce them, using smart metering. The results showed that the cost of electricity for the technical power losses in the chosen area is very high. In order to reduce these costs, a number of measures for ETL optimization and reduction were proposed.

A novel smart metering technique capable of anomaly detection was proposed for real‐time home power management system. Smart meter data generated in real‐time were obtained from 900 households of single apartments. To detect outliers and missing values in smart meter data, a deep learning model, the autoencoder, consisting of a graph convolutional network and bidirectional long short‐term memory network, was applied to the smart metering technique. Power management based on the smart metering technique was executed by multi‐objective optimization in the presence of a battery storage system and an electric vehicle. The results of the power management employing the proposed smart metering technique indicate a reduction in electricity cost and amount of power supplied by the grid compared to the results of power management without anomaly detection.

Experimental investigation on wireless integrated smart system for energy and water resource management in Indian smart cities

In this chapter, a survey of protocols for privacy-preserving data aggregation in smart metering systems is presented. They are necessary to protect customers'profiles from adversaries, who can predict customers' behaviour and manipulate them. The state of the art covers the entire cycle of a smart grid with real-time consolidated consumption, billing processes and verifications. However, the real-time consolidated consumption is public in the best protocol. In this work, we find an improved protocol that can hide real-time consolidated consumptions keeping them accessible only to the public utility. In addition, a complexity analysis of algorithms is presented and a simulation with millions of real-world measurements collected by thousands of smart meters is run to validate the theoretical evaluation. Furthermore, the improved protocol slightly outperforms the state of the art in the encryption algorithm, which runs in constrained smart meters. Moreover, such techniques can be applied in several other research areas.

With the exponential rise of pervasive computing applications, data privacy has become much more of an important issue than before. When data is aggregated at each hop in a sensor network, it becomes harder to protect its privacy. A number of privacy preserving data aggregation algorithms have recently appeared for wireless sensor networks (WSNs), very few of them however also address the issue of data integrity along with privacy. Data privacy and integrity are two contrasting objectives to achieve in general. In a privacy preserved data aggregation, it becomes easier for an attacker to inject false data hence, we suggest that both privacy and integrity of data should be treated together. In this paper, we present an energy efficient, privacy preserving data aggregation algorithm which also preserves data integrity in WSNs. We analyze the security of the algorithm and provide proofs for confidentiality and integrity. We enhance this algorithm further to localize, to a certain degree, the corrupt aggregator. We provide the results of our implementation of the algorithm on TelosB motes, illustrating that both the computational overhead and the energy consumption are very low. Finally, we compare our algorithm with other schemes having similar objectives demonstrating that our algorithm performs better in terms of band with usage and energy consumption in a WSN environment.

The social dimensions of smart meters in the United States: Demographics, privacy, and technology readiness

Power quality and reliability issues are big challenges to both service provider and consumers in conventional power grids. The ongoing technological advancements in the Internet of Things (IoT) era provide better solutions to enhance the management of these challenges and enforce the measures of a smart grid (SG). Advanced metering infrastructure (AMI) and smart meter (SM) technologies are enabler technologies that can modernize the conventional power grid through exposing the hidden details of electrical power by introducing a two-way communication scheme during power transaction process between utilities and consumers. Throughout literature, AMI and SM technologies are widely discussed. However, few studies discuss the role of SM in power quality and reliability monitoring in IoT-enabled SGs. Hence, this chapter aims to comprehensively review the feasibility of using SM for power quality and reliability monitoring. First, we provide a detailed overview of the SMs, wireless communication technologies, and routing algorithms as enabling technologies in AMI. Then, we categorize the existing literature works that target power quality and reliability monitoring. Finally, open research issues are outlined based on shortages in the existing literature.

Research has shown that feedback on energy use can aid households to reduce it significantly. In this context, smart metering technologies, and more specifically technology components interacting with data gathered and provided by a smart meter, allowing to provide the consumer with personalized feedback, consumption visualization, automated control,… could play an important role. After all, by means of this technology, households can be made more aware of their energy use and encouraged to reduce it. This paper applies a user centered approach towards the estimation of the adoption potential for smart metering technologies in Flanders, Belgium. We conducted a representative quantitative survey with 1314 respondents living in Flanders. A segmentation on ownership of, attitude towards and adoption intention of smart metering devices was performed on the data. Traditional approaches of intention surveying often result in an overestimation of the innovation adoption potential. To overcome this problem, the Product Specific Adoption Potential scale (or PSAP-scale) was used and 6 segments were found. These segments were labeled “Current Owners”, “Innovators”, “Early Adopters”, “Early Majority”, “Later Adopters” and “Out of Potentials. The verification of the adoption potential of smart metering devices for different pricing scenarios revealed a rather high price sensitivity.

Smart water meters at household connections are being installed in large numbers throughout the world due to the benefits they are expected to bring to the water utilities and water consumers. Smart metering provides high‐resolution readings and promises benefits to the water utilities, such as demand forecasting, regulating time‐of‐use watering, and making intelligent operation and planning decisions. For the consumers, smart metering promises improved billing and demand reduction by providing detailed and timely information about their water use and early notification of possible water leaks in their premises. However, the fine‐grained information collected by smart meters raises growing concerns of privacy invasion due to personal behavior exposure (private activity, daily routine, etc.). Nevertheless, there is no readily available technology for protecting water consumers from revealing their in‐home private activities. Thus, a viable argument in favor of smart metering technologies will not be possible without proactively accounting for the associated privacy challenges. Here, we present a practical technology coupling a dedicated apparatus with a control model for increasing personal privacy. We quantify the level of privacy achieved using information‐theoretic criterion and an empirically based occupancy detection method between the smart meter readings and actual water use. Furthermore, we evaluate and compare privacy protection using the best effort approach previously developed for masking activities revealed from smart electricity meters. The main results reveal that simple control actions can disguise personal behavior patterns and, thus, hedge against privacy breach in smart water meters. Furthermore, we quantify the trade‐off between the size of the apparatus and the level of privacy protection it provides. Our results demonstrate how “privacy friendly” smart water metering technology could be implemented in real‐life systems and reduce the privacy concerns of water consumers.

Cross-border energy trading activity via interconnection has received much attention in Southern Asia to help the South Asian Association for Regional Cooperation (SAARC) region’s energy deficit states. This research article proposed a smart metering system to reduce energy losses and increase distribution sector efficiency. The implementation of smart metering systems in utility management plays a pivotal role in advancing several Sustainable Development Goals (SDGs), i.e.; SDG (Affordable and Clean Energy), and SDG Climate Action. By enabling real-time monitoring, accurate measurement, and data-driven management of energy resources, smart meters promote efficient consumption, reduce losses, and encourage sustainable behaviors among consumers. The adoption of a smart metering system along with Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis, socio-economic analysis, current challenges, and future prospects was also investigated. Besides the economics of the electrical distribution system, one feeder with non-technical losses of about 16% was selected, and the cost–benefit analysis and cost–benefit ratio was estimated for the SAARC region. The import/export ratio is disturbing in various SAARC grids, and a solution in terms of community microgrids is presented from Pakistan’s perspective as a case study. The proposed work gives a guidelines for SAARC countries to reduce their losses and improve their system functionality. It gives a composite solution across multi-faceted evaluation for the betterment of a large region.

This paper describes the importance of the interoperability in smart metering systems for modern distribution system operators. The lack of interoperability significantly increases the costs of smart metering system implementation for distribution system operators, especially the costs of purchasing new equipment. The replacement, cleanup and maintenance of electricity meters produced by different manufacturers, which are not interoperable, are also much more complex and expensive. Furthermore, without the interoperability, the system cannot be observable, i.e. the observability exists only for some particular segments of the system. This prevents many functionalities of distribution management system. In terms of interoperability, smart metering systems have gone through four different stages. A comparison of the system implementation costs for these four stages is presented for distribution system operator of Serbia.

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.

Bottled gas smart metering is among the strategic innovations which have transformed the energy sector in Africa. Research has proved that using this measuring device on bottled gas (liquefied petroleum gas) increases access to clean cooking among low-income households. Smart metering technology cuts down the initial investment costs and the running costs which are the major barriers to the utilization of LP gas. Apart from scaling up the adoption of modern1 clean cooking fuels, the diffusion of smart metering innovation offers various technological opportunities which can help to promote economic growth; but it is also associated with several technological challenges which may adversely affect economic progress.KeywordsTechnologySmart meterBottled gasPay-as-you-CookOpportunityChallenge

The roll-out of the new-generation smart meter with artificial intelligence (AI)-based data mining algorithms causes serious privacy issues for consumers. By detecting appliance usages, an adversary can easily monitor the behavior patterns of residents. In this paper, a privacy-preserving smart metering model is proposed; the system utilizes a data aggregator to aggregate the readings of neighboring smart meters and a data down-sampler to reduce the sensitive information in the load profiles. An AI-based adversary is introduced to simulate the adversarial process. Four state-of-the-art deep learning/machine learning algorithms (convolutional neural network–long short-term memory (CNN-LSTM), gated recurrent unit (GRU), k-nearest neighbors (KNN), and CNN are employed as data mining algorithms. By tuning the variables (aggregation size α and interval resolution σ), the detectability boundaries of particular appliances are evaluated. Based on the appliance detectability, a three-level privacy boundary (real-time surveillance, presence/absence detection, and complete protection) is obtained. The result shows that to achieve complete data protection, the aggregation size should exceed 40, and the interval resolution should exceed 8 hours.