A Homomorphic-Based Multiple Data Aggregation Scheme for Smart Grid

Abstract

Smart meters have been installed to report users' real-time electricity consumption data to the utility supplier periodically, which enables fine-grained energy supply, as the utility supplier can adjust its supplement based on users' consumptions. However, these real-time electricity data can also reveal the behaviors of the inhabitants; for example, the real-time electricity consumption data can reveal if the inhabitant is at home, if the televisión is working, and so on. People are reluctant to disclose these kinds of personal information. In this paper, we come up with a smart meter data aggregation scheme based on the Paillier homomorphic cryptosystem, this aggregation scheme enables a utility supplier to get the total consumption of all the smart meters, while the utility supplier is unable to get the consumption data of a single smart meter. In addition, the proposed scheme enables the smart meter to report múltiple types of data in one reporting message, which makes it possible for the supplier to conduct the variance analysis and the one-way analysis of variance on the data. The formal security analysis shows that the proposed scheme is semantically secure. The experiment results show that the proposed scheme can reduce the computation cost both on the smart meter side and on the aggregator side.

Smart Grids and Their Role in Transforming Human Activities—A Systematic Literature Review

“…Likewise, we find studies with a very similar final goal, which is the analysis and evolution of SGs and applications to improve people's lives, such as in [44,52,55,63,90,91].…”

An Optimal Data Aggregation Scheme for Wireless Sensor Network Using Qos Parameters With Efficient Failure Detection and Loss Recovery Technique

“…When the clustering node obtains the binary data's from the source node, it will recognize the node failures of the source location based on the received data by categorizing the faulty data and using SVMSFDLA technique minimizes the packet drop, delay, transmission overhead and increases reliability. From the existing papers [21][22][23][24][25][26][27][28][29][30], the authors have concentrated only on to detect the faults rather than the corresponding loss recovery system. SFDLA technique [31] overcomes that problem by the combined contributions, but the employed SVM-based failure detection consumed much more power.…”

“…The compromising points of this method are cost function. And it is computed from the distance between the gateway to BS (Base Station) and the gateway of next-hop to BS along with the cost estimation of BS and the gateway residual energy of the next-hop which is complex and less attractive Chen et al [22] came up with a scheme called privacy-preserving data aggregation for secure smart grid communication with fault tolerance (PDAFT). They have utilized an encryption method known as Homomorphism Parlier Encryption to secure the sensitive information of the user, because of this over cover makes the base station about no clue about the originating node of the received data.…”

“…In the second scenario, we vary the number of faults by 5, 10, 15, 20, and 25 with fixed number of nodes as 120. The total simulation will take the time period of 1000 s. The performance of proposed Q-ODA technique is analysed with the prevailing techniques and specifically DFTR [21], PDAFT [22], FIDA [24], EMDC [28], MDFU [30] and SFDLA [31]. The comparisons are done in Algorithm 2 Fault detection using multi-criteria moth-flame decision-making (MMFD).…”

“…SH acts as H in the event of header node failure 4. The proposed Q-ODA is computed using network simulator 2 tool and the results are compared with the existing methodologies namely DFTR [21], PDAFT [22], FIDA [24], EMDC [28], MDFU [30] and SFDLA [31] and the simulation results emphasis our proposed work an edge over than the above methodologies specifically in relations with energy consumption, End-to-End delay, data delivery ratio, data accuracy, network lifetime, failure rate, and cost of data aggregation.…”