Efficient Privacy-Preserving Multi-Dimensional Data Aggregation Scheme in Smart Grid

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

Recent development of smart cities includes advanced and necessary use of modern smart grid (SG), than the traditional power grid. The paradigm of SG has also transformed houses into a home area networks (HAN). In HAN, several smart devices and appliances are connected to the electricity control centers (ECC). Appliances share their load and consumption related information to ECC through smart meters. The consumption data may be used for supply-demand management, for example, by ramping production up or down as needed. However, security and privacy of the consumers data are greatly important, since fine-grained smart meter data may reveal an users presence/absence in his/her house. To address this issue, several public-key-based or identity-based data aggregation schemes have been proposed in the literature. However, most of such schemes either suffer from the complexity of certificate management or key escrow problem. To eliminate these issues, in this paper we propose an efficient certificate-based data aggregation (CB-DA) scheme. In the proposed CB-DA scheme, the owner selects a secret key and then use the secret key along with certificates as decryption/signing keys.

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.

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 aggregation scheme resisting on malicious data mining attacks for smart grid

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.

With the wide use of distributed energy resources, it is important to build efficient and trustworthy coordination among source, grid, load, and storage (SGLS) for the Energy Internet. Blockchain can provide a base of trust, but getting off-chain data through decentralized oracles still faces problems of low efficiency and poor reliability. To address these issues, this paper proposes a four-layer architecture that joins blockchain and oracle services. It also includes a data aggregation and checking algorithm based on threshold signatures and a reputation-based oracle selection method. The main idea of the algorithm is to find reliable nodes more efficiently by using a changing, multi-factor reputation model, and to make the aggregation process faster through preselection and threshold signatures. In this way, it keeps both reliability and efficiency in complex network settings. Simulation results show that the proposed method increases the speed of putting data on the chain and lowers delay, while enhancing the robustness of the oracle network under adverse network conditions. This work providesuseful technical support for building an efficient and dependable distributed-energy coordination infrastructure.

Data aggregation scheme for IOT based wireless sensor network through optimal clustering method

A privacy-preserving data aggregation system based on blockchain in VANET

Many wireless sensor network (WSN) applications require privacy of the sampled data during transmission from the source nodes to a data collecting device, say a query server. Providing an efficient data aggregation scheme with preserving data privacy is a challenging problem in WSNs. Although the secure data aggregation in WSNs has been well studied in the recent years, there exists a little work, for instance PDA (Privacy-preserving Data Aggregation), which focuses on protecting sensor data not only from adversaries but also from the participating trusted sensor nodes. However, PDA suffers from two problems. The first one is high communication cost due to unnecessary traffics in the network during data transmissions. The second one is high computation cost due to the use of expensive technique to customize sensor data. To resolve the problems, we, in this paper, propose a new private data aggregation scheme for WSNs. The proposed scheme applies the additive property of complex numbers in order to combine sensor data and preserve data privacy during transmissions to the query server. We show from our analytical performance evaluations that our scheme is more efficient than the PDA scheme in terms of both communication and computation costs.

The wireless communication nature of remotely deployed sensor nodes make the attacks more easily to be happened in wireless sensor networks (WSNs). But traditional security algorithms are infeasible in WSNs due to the limited computing, communication power, storage, band width and energy of sensor nodes. So energy efficient secure data aggregation schemes are necessary in resource constrained WSNs. Concealed Data Aggregation (CDA) based on privacy homomorphism (PH) gives a critical solution for energy efficient secure data aggregation in WSNs. PH based algorithms allow aggregation to be happened on cipher texts. Thus, it eliminates the power consuming decryption operations at the aggregator node for the data aggregation and further encryption for the secure transmission of aggregated data. It also avoids the aggregator node from the burden of keeping the secret key information and thereby it achieves energy efficiency and reduces the frequency of node compromise attacks in aggregator nodes. Among the CDA techniques, asymmetric PH based CDA techniques are exploring due to their combination with elliptic curve cryptography having reduced key size. We present an overview of asymmetric concealed data aggregation techniques that achieve both end to end data confidentiality and non delayed data aggregation.

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.

Research on the privacy protection of smart grids mostly stays in fixed electricity prices or electricity aggregation. Aiming at the problem of real-time pricing and privacy data protection of smart grid, this paper proposes a privacy-preserving electricity consumption statistics and billing scheme in smart grid (PPCSB). In this scheme, the techniques of additive homomorphic encryption and mixed multiplicative homomorphic encryption are used to ensure the security of the data communication; Batch verification is used to improve the efficiency of signature verification. In addition, the proposed scheme does not require a trusted third party in order to improve the usability of the scheme. Through performance analysis of the scheme, it shows that the scheme has better security and better functions.

Privacy-preserving data aggregation (P2DA) is an important basic building block that can protect consumer’s privacy in the smart grid environment because it could be used to prevent the extraction of the electricity consumption information of a specific consumer. Due to this important function, the P2DA scheme for the smart grid has attracted a lot of attention from both academic and industry researchers who have proposed many P2DA schemes for the smart grid in recent years. However, most of these P2DA schemes are not secure against internal attackers or cannot provide data integrity. Besides, their computation costs are not satisfactory because the bilinear pairing operation or the hash-to-point operation is performed at the smart meter’s side. To address the deficiencies of previous schemes, we propose a new P2DA scheme against internal attackers using Boneh–Goh–Nissim public key cryptography. The proposed P2DA scheme does not use bilinear pairing or hash-to-point operation making it be more computationally efficient than previous P2DA schemes. We also show that the proposed P2DA scheme is provably secure and can meet various security requirements.

Advancing network technologies allow the setup of two-way communication links between energy providers and consumers. These developing technologies aim to enhance grid reliability and energy efficiency in smart grids. To achieve this goal, energy usage reports from consumers are required to be both trustworthy and confidential. In this paper, we construct a new data aggregation scheme in smart grids based on a homomorphic encryption algorithm. In the constructed scheme, obedient consumers who follow the instruction can prove their data consumption's adjustment by using a range proof protocol. Additionally, we propose a new identity-based signature algorithm in order to ensure authentication and integrity of the constructed scheme. By using this signature algorithm, data usage reports are verified in real time. Extensive simulations demonstrate that our scheme outperforms other data aggregation schemes.