Enhancing user privacy by preprocessing distributed smart meter data

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.

Recently proposed trusted processor model is a promising model for building secure applications. While effective designs have been proposed for protecting data confidentiality and data integrity in such environments, an important security criterion -- user privacy is usually neglected in current designs. Due to the increasing concern of privacy protection in the Internet era, such deficiency can hinder the adoption of the new model.In this paper, we identify the threat model to user privacy and propose a new scheme for user privacy protection. In addition to providing the same ability in protecting data confidentiality and data integrity, the new scheme effectively protects user privacy and only introduces very low overhead.

Since the development of location-based services, privacy-preserving has gained special attention and many algorithms aiming at protecting user's privacy have been created such as obfuscation or k-anonymity. However, all of these researches separate the algorithms from the database level. Thus, the querying process has two phases, querying the database to retrieve the accurate positions of users and then modifying them to decrease the quality of location information. This two-phase process is time-consuming due to the number of disk accesses required to retrieve the user's exact position. We address this problem by proposing OST-tree, a structure that embeds the user's privacy policy in its node and obfuscates the spatio-temporal data. Experiments show that OST-tree provides an improvement over the algorithm separated from the database level for both querying costs and user's privacy protection.

While real-time electricity consumption data of users in smart grids can enable value-added services, such as Big Data analytics, each individual user's privacy needs to be protected. How to balance data utility and privacy protection is a significant issue, of which privacy-preserving data aggregation (PPDA) is a viable solution. Prior to this, researchers have proposed a number of PPDA schemes to address the above challenge. Unluckily, most of them suffer from security and privacy drawbacks, while others are inappropriate for resource-limited smart meters due to high cost of cryptographic operations. To tackle this issue, in this article, we propose a pairing-free and exponentiation-free certificateless PPDA scheme named CL-PPDA for smart grids. We prove the security of our design and analyze its performance. Comparative analyses with state-of-the-art work in theory and experiment show that our design not only has better security properties, but also has competitive computation overhead, especially on the resource-constrained smart meter side. Besides, we present an extension of our CL-PPDA scheme to support multidimensional data aggregation, which further enriches the functionality of our design.

We developed a secure processor that protects foreign software running on an open source operating system (OS). The implementation of such a processor has not been reported yet. The processor is called L-MSP (License-controlling Multi-vendor Secure Processor). In this paper, the relationship between software protection and user privacy is discussed. In general, software protection enables secure distributed computing and copyright protection, but the software might violate user privacy. In the L-MSP system, user privacy is able to be protected by security sandbox and related OS mechanisms which is implemented in an open source. Thus transparency of user privacy protection can be assured on the system.

With the rise of digital images in our daily lives, there is a growing need to provide an image trading market where people can monetize their images and get desired images at prices that fit their budget. Those images are usually uploaded and stored onto centralized image trading service providers’ servers and the transactions for image trading are processed by these providers. Unfortunately, transaction unfairness and users’ privacy breaches have become major concerns since the service providers might be untrusted and able to manipulate image trading prices and infer users’ private information. Recently, several approaches have been proposed to address the unfairness issue by using the decentralized ledger technique and smart contract, but users’ privacy protection is not considered. In this paper, we propose a fair and privacy-preserving protocol that supports image fair exchange and protect user privacy. In particular, we exploit blockchain and Merkle tree to construct a fair image trading protocol with low communication overhead based on smart contract, which serves as an external judge that resolves disputes between buyers and sellers in image transactions. Moreover, we extend a popular short group signature scheme to protect users’ identity privacy, prevent linkability of transactions from being inferred, and ensure traceability of malicious users who may sell fake images and/or refuse to pay. Finally, we design and build a practical and open-source image trading system to evaluate the performance of our proposed protocol. Experimental results demonstrate its effectiveness and efficiency in real-world applications.

In this paper, we investigate the use of decentralized blockchain mechanisms for delivering transparent, secure, reliable, and timely energy flexibility, under the form of adaptation of energy demand profiles of Distributed Energy Prosumers, to all the stakeholders involved in the flexibility markets (Distribution System Operators primarily, retailers, aggregators, etc.). In our approach, a blockchain based distributed ledger stores in a tamper proof manner the energy prosumption information collected from Internet of Things smart metering devices, while self-enforcing smart contracts programmatically define the expected energy flexibility at the level of each prosumer, the associated rewards or penalties, and the rules for balancing the energy demand with the energy production at grid level. Consensus based validation will be used for demand response programs validation and to activate the appropriate financial settlement for the flexibility providers. The approach was validated using a prototype implemented in an Ethereum platform using energy consumption and production traces of several buildings from literature data sets. The results show that our blockchain based distributed demand side management can be used for matching energy demand and production at smart grid level, the demand response signal being followed with high accuracy, while the amount of energy flexibility needed for convergence is reduced.

Smart meter data analysis is essential for balancing energy consumption and minimizing power outages. However, high-resolution smart meter readings pose challenges to data analysis due to their high volume and dimensions. We propose Online-FDA, an online functional load demand analysis and forecasting framework that incorporates real-time smart meter readings with adaptive clustering to identify daily patterns in functional load consumption and predict daily load demands. This framework utilizes a model-based functional clustering approach assisted by the intra-day load consumption attributes to analyze real-time smart meter data. Moreover, the Online-FDA augments the clusters with a state-of-the-art functional deep neural network that utilizes the training-testing-updating strategy to adaptively learns from real-time smart meter data. Experimental results with real-world smart meter data showed that the proposed Online-FDA is superior to other benchmark algorithms for capturing time-varying variations in load demand, which are essential to the real-time control of electricity grids and the planning of power systems.

Vast amounts of information are collected and organized into a more understandable and comprehensive style through data aggregation. User privacy needs to be maintained even though real-time electricity use data is helpful for big data analysis. One of the main issues is finding a balance between data utility and privacy protection. To ensure secure transmission, the individual measurements from each user should be combined before being transferred to the gateways. This work presents a quantum protocol-based security architecture for user data that leverages entanglement based techniques and quantum key distribution to enhance the security and privacy of data aggregation. Safe information transport is made possible via quantum entanglement, which solves the shortcomings of conventional cryptography methods. Using quantum protocols, our method maintains its robustness and efficiency while providing an extra degree of security. We show that neither external nor participant attacks may compromise our protocol.

Smart meter enabled water end-use demand data: platform for the enhanced infrastructure planning of contemporary urban water supply networks

In the smart grid, Advance Metering Infrastructure (AMI) are used to obtain huge amount of data sampled from the electrical users for the various advanced applications (e.g., demand side response and gird operation). However, there exist the leakage risk of users' privacy in the smart data collection process. To solve this problem, this paper proposes a smart meter data encryption method based on the random forest (RF) algorithm and aggregate signature method. Firstly, the electrical data is decomposed into the components with different time scales by using the empirical mode decomposition (EMD) method. Then the RF algorithm is used to extract the electrical consumption behavior as the criterion for judging the data authenticity of AMI. The components and features of electrical information are regarded as the data of AMI that need to be transmitted. Secondly, a novel framework combined with the disposable pseudonym algorithm, aggregate signature algorithm and homomorphic encryption Paillier algorithm is designed to encrypting the transmitted data. Finally, the numerical results show that compared with the traditional machine learning methods, the proposed RF algorithm improves the classification accuracy of data authenticity by more than 2.89%. Moreover, compared with the traditional encryption methods, the time cost of the proposed method can be saved by more than 4.5%. Hence, the privacy of users' information can be protected more effectively.

Driven by the information age, a paradigm shift is starting to occur in the urban water planning and management field, mainly due to the introduction of advanced metering and communications technologies. This chapter explores the potential role of intelligent water metering systems, and the smart application of resultant data sets for the future of water planning and management in the built environment. Intelligent water meters essentially perform three functions; they automatically and electronically capture, collect and communicate up-to-date water usage readings on a real-time (or nearly real-time) basis. To translate this data into useful information, a comprehensive intelligent water metering system is required, which integrates high-resolution metering (e.g., 0.01?l; 5?sec), communication technologies (e.g., GPRS) and information management systems, in order to provide real-time information on how, when and where water is being consumed for the customer and water business. This chapter seeks to promote the uptake and application of intelligent water metering systems by examining the benefits and drivers of, and barriers to, intelligent metering. Specifically, it presents applicable examples of where intelligent metering systems can significantly enhance current activities in terms of citywide urban planning, infrastructure planning, water demand management and customer satisfaction.

Patient can obtain sorts of health-care delivery services via Telecare Medical Information Systems (TMIS). Authentication, security, patient's privacy protection and data confidentiality are important for patient or doctor accessing to Electronic Medical Records (EMR). In 2012, Chen et al. showed that Khan et al.'s dynamic ID-based authentication scheme has some weaknesses and proposed an improved scheme, and they claimed that their scheme is more suitable for TMIS. However, we show that Chen et al.'s scheme also has some weaknesses. In particular, Chen et al.'s scheme does not provide user's privacy protection and perfect forward secrecy, is vulnerable to off-line password guessing attack and impersonation attack once user's smart card is compromised. Further, we propose a secure anonymity authentication scheme to overcome their weaknesses even an adversary can know all information stored in smart card.

This work is aimed at crea Irkutsk ting a domestic switching IT platform that organizes commercial and technological data exchange with intelligent metering devices within the Kumir-Resource complex. To facilitate efficient control and management of the modern pipe line and electric grid infrastructure an IoT (Internet of Things) platform can be built to organize communication with a multi-million audience of end consumers using smart metering devices. Implementation of such a platform will allow centralized collection, monitoring, management, analysis and visualization of all pipeline system data.

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.