Lightweight Privacy-preserving Data Aggregation Research Articles

A Robust and Lightweight Privacy-Preserving Data Aggregation Scheme for Smart Grid

Privacy-preserving data aggregation (PPDA) enables data availability and privacy preservation simultaneously in smart grid. However, existing methods, such as masking and homomorphic encryption, cannot simultaneously offer strong privacy preservation, fault tolerance for both smart meters and aggregators, verifiable aggregation, and lightweight encryption. To tackle these challenges, we design HTV-PRE, a homomorphic threshold proxy re-encryption scheme with re-encryption verifiability. HTV-PRE involves only linear operations and resists quantum attacks after being instanced by ideal lattices. By leveraging HTV-PRE, we propose a robust and lightweight data aggregation scheme with strong privacy preservation for smart grid. Robustness ensures fault tolerance and error detection. Even if some smart meters or aggregators are faulty, data aggregation can still work without imposing expensive computation on other smart meters or requiring additional trust assumptions. Additionally, to detect aggregators' errors, a proof for the aggregated result is presented so that anyone can verify whether the result has been correctly computed or not. The verifiable aggregation adds no computation/communication overhead on the user side. The performance evaluations demonstrate that our PPDA scheme significantly offloads computation overhead from smart meters and control center to the edge, and its user encryption is up to 4x faster than existing approaches.

Privacy-preserving data aggregation scheme against deletion and tampering attacks from aggregators

Because privacy-preserving data aggregation protocols provide data privacy and data compression, they have been extensively studied in smart grids. However, most of the existing data aggregation protocols are based on honest-but-curious aggregators, or adopt computationally intensive asymmetric homomorphic encryption, making these protocols only provably secure in weak security models, or resulting in high computational complexity for terminal users. To address the aforementioned issues, in this work, we propose a novel lightweight privacy-preserving data aggregation scheme against malicious aggregators based on our symmetric homomorphic encryption. The proposed scheme can reduce the computational cost of smart meters while also resisting tampering and deletion attacks from malicious aggregators. One of the highlights of the proposed scheme is its ability to withstand deletion attacks from malicious aggregators, which makes it unnecessary for the malicious aggregator to aggregate the readings of all smart meters instead of only a part of them. Therefore, our scheme is more secure than other schemes. It is the first privacy-preserving data aggregation scheme based on a stronger security model without multiple rounds of interaction. Finally, the detailed security analysis shows that the proposed scheme satisfies desirable security properties. Moreover, experimental results demonstrate that the proposed scheme is superior to the other four schemes in terms of computational complexity and communication overhead.

Edge Blockchain Assisted Lightweight Privacy-Preserving Data Aggregation for Smart Grid

Compared with traditional power systems, smart grid is designed to provide effective and secure energy services. Data aggregation is one of the key technologies in wireless sensor networks, which reduces the amount of data transmission between nodes by merging similar data and simplifying redundant data, thus significantly reducing the computation cost and communication overhead of the system. Many data aggregation schemes have been developed for the smart grid in the past years. However, most of the data aggregation schemes ignore the data security and privacy protection issues of the edge layer. To solve these problems, in this article, we propose an edge blockchain assisted lightweight privacy-preserving data aggregation for smart grid, named EBDA. In this work, we integrate edge computing and blockchain to design a three-layer architecture data aggregation scheme for smart grid. This new architecture supports a two-level data aggregation scheme, which is more efficient and secure. Through theoretical analysis and simulations, EBDA shows great superiority in terms of resisting network attacks, reducing system computation costs and communication overhead compared with existing schemes.

Lightweight privacy-preserving data aggregation protocol against internal attacks in smart grid

Privacy-preserving data aggregation has been studied extensively over the past decades, but there are still some concerns remained. For example, some schemes cannot resist against internal attacks, especially when the internal attack is launched by either the data centers that allocate the system security parameters or the attacker who shares the common information with the targeted user. In this paper, we propose a lightweight privacy-preserving data aggregation scheme, which is more efficient and suitable for the resource-constrained devices. Our scheme aggregates total electricity consumption data in the smart grid, with the capability of resisting against the collusion attacks with (n − 1) users. In the evaluation, we investigate the performances in the aspects of computation and communication costs as compared with the state-of-the-art, and the results show that our scheme is practical for the current smart grid environment.

Lightweight privacy preserving data aggregation with batch verification for smart grid

By analyzing the electricity usage data collected from users in a smart grid, the grid provider can flexibly adjust the power distribution policy. In such a setting, it is also important to ensure the confidentiality of users’ electricity usage data and the privacy of users. In this paper, we propose an efficient scheme for a smart grid that achieves lightweight aggregation, based on a novel symmetric homomorphic encryption scheme. In addition, we design an authentication agreement scheme that builds upon the password authenticated key exchange (PAKE) protocol. Our security analysis demonstrates that our proposed scheme guarantees the confidentiality and integrity of electricity usage data, as well as the privacy of users. Evaluation of the performance of our proposed scheme also demonstrates that we achieve efficient data aggregation, with only minimal computational overhead generated at the smart meter. A comparative summary of our proposed scheme with three other schemes shows that our proposed scheme is more secure and efficient.

A secure and lightweight privacy-preserving data aggregation scheme for internet of vehicles

Data aggregation can effectively improve resource utilization in Internet of Vehicles (IoV), and has attracted broad attention recently. However, because of the particularity of nodes in IoV, data aggregation faces security challenges such as location privacy, data privacy and accuracy of aggregation results. To solve these challenges, in this paper, we propose a secure and lightweight privacy-preserving data aggregation scheme, named SLPDA, for IoV. Specifically, SLPDA employs Chinese Remainder Theorem to map multi-dimensional data to one-dimensional data, and uses masking technology to encrypt data, thus roadside units can realize lightweight data aggregation without plaintext. SLPDA also adopts Identity-based batch authentication technology to reduce authentication overhead. Detailed security analysis indicates that SLPDA can resist various security threats. In addition, the performance evaluations demonstrate that SLPDA is more efficient than the reported schemes in terms of computation complexity and communication overhead.

A lightweight privacy-preserving data aggregation scheme with provable security for internet-of-things

In this work, an autonomous lightweight certificateless aggregate signature-based data aggregation scheme for internet-of-things (IoT) is proposed. In the scheme, IoT terminals only need to contact the key generation center (KGC) once. Subsequently, they can generate their pseudonyms on their own without any interaction with the KGC. An efficient revocation mechanism is provided to punish malicious terminals. The proposed scheme is anonymous, can provide conditional traceability, resist various attacks, and solves private key compromise problem. The formal security proof demonstrates that the scheme is secure against both Type I and Type II adversaries in the random oracle model based on the intractability of the Discrete Logarithm Problem. The performance evaluation shows that the proposed scheme is more efficient than the state-of-the-art schemes and fits the resource-constraint IoT environment.

Lightweight privacy-preserving data aggregation scheme for smart grid metering infrastructure protection

The electric industry's planned shift to smart grid metering infrastructure raised several concerns especially about preserving the privacy. Various data perturbation and aggregation solutions are being developed to address these concerns. The drawback of these solutions is that a simple random noise scheme cannot protect privacy, and there is a need for more advanced perturbation techniques to increase hardware costs of smart metering devices. The proposed data aggregation scheme combines the power of perturbation techniques with crypto-systems in an efficient and lightweight way so that it becomes applicable for devices with limited hardware, such as smart meters. We investigated the privacy preserving capabilities of the proposed aggregation scheme with Holt-Winters and Seasonal Trend Decomposition using Loess prediction methods. The results indicate that the proposed scheme is resilient to both filtering and true value attacks.

Lightweight and efficient privacy-preserving data aggregation approach for the Smart Grid

Over the last few years, we have seen the emergence of a wide range of Smart Grid architectures, technologies, and applications made possible by the significant improvements in hardware, software, and networking technologies. One of the challenges that has emerged in the Smart Grid environment is the privacy of Smart Grid users. Although several privacy-preserving techniques have been proposed recently for the Smart Grid environment, many of them suffer from high computation and communication costs, different types of attacks, and the use of complex key management schemes. To address these drawbacks, we propose an efficient, lightweight privacy-preserving data aggregation approach that makes use of symmetric homomorphic encryption and Diffie–Hellman (DH) or Elliptic Curve Diffie–Hellman (ECDH) key exchange methods. In contrast to previously proposed privacy-preserving schemes for the Smart Grid, we demonstrate the superiority of our proposed approach in terms of its low transmission and message overheads, and resiliency against a wide range of session key attacks, and ability to maintain data integrity against unauthorized modification or data forgery and to ensure authenticity of smart meters’ data.

A Lightweight Privacy-Preserving Data Aggregation Scheme for Fog Computing-Enhanced IoT

Fog computing-enhanced Internet of Things (IoT) has recently received considerable attention, as the fog devices deployed at the network edge can not only provide low latency, location awareness but also improve real-time and quality of services in IoT application scenarios. Privacy-preserving data aggregation is one of typical fog computing applications in IoT, and many privacy-preserving data aggregation schemes have been proposed in the past years. However, most of them only support data aggregation for homogeneous IoT devices, and cannot aggregate hybrid IoT devices’ data into one in some real IoT applications. To address this challenge, in this paper, we present a lightweight privacy-preserving data aggregation scheme, called Lightweight Privacy-preserving Data Aggregation, for fog computing-enhanced IoT. The proposed LPDA is characterized by employing the homomorphic Paillier encryption, Chinese Remainder Theorem, and one-way hash chain techniques to not only aggregate hybrid IoT devices’ data into one, but also early filter injected false data at the network edge. Detailed security analysis shows LPDA is really secure and privacy-enhanced with differential privacy techniques. In addition, extensive performance evaluations are conducted, and the results indicate LPDA is really lightweight in fog computing-enhanced IoT.