Lightweight privacy protection data multistage aggregation method based on fog computing

Abstract

The invention discloses a lightweight privacy protection data multistage aggregation method based on fog computing. Firstly, a power grid coverage area is divided into sub-areas; a fog node exists under each subarea; a plurality of intelligent electric meters exist under the fog node; the intelligent electric meter collects power consumption information of a user in real time and uploads an encrypted data generation report to the unique fog node in the subarea; the fog node collects power utilization data from all the intelligent electric meters in the sub-region, performs primary aggregation,generates a report and uploads the report to the cloud server; and the cloud server performs secondary aggregation on the data from all the fog nodes and generates a report, then decrypts and reads the aggregated data by using the key so as to control the power consumption, and the trusted institution is used for generating system parameters and registering the intelligent electric meter and thefog equipment in the data aggregation process. The method has the advantages of high flexibility, low calculation cost, high analysis speed, high precision and the like.

Description

technical field [0001] The invention relates to the field of data processing of the Internet of Things, in particular to a multi-level aggregation method of lightweight privacy-protected data based on fog computing. Background technique [0002] With the development of wireless communication technology, various intelligent sensing devices have emerged. Smart devices can collect data and transmit the data to the cloud center for real-time observation and intelligent decision-making. Therefore, concepts such as smart home, smart medical care, smart city, and smart grid have been proposed one after another, and smart grid has received more and more attention as the next generation grid. In a smart grid system, distributed smart meters generate data reports and transmit them to a remote cloud server via the Internet for further analysis. The cloud server can regularly monitor power delivery and consumption information for real-time decision-making. However, these smart meters w...

AI Technical Summary

Problems solved by technology

When the user's power consumption increases significantly, the battery is discharged, and vice versa, the battery is charged to hide the user's real-time power consumption, but frequent battery discharge and charge will shorten the battery life

In other words, these operations, while offloading the computing and communication burden of the smart grid, require frequent maintenance, which is very expensive

The second category is to add noise at the smart meter and remove it at the control center to cover up the real reading of the smart meter. Although these methods can achieve the protection of private data, but due to the operation of adding noise, the control center cannot accurately reconstruct The real reading of the smart meter, for some scenarios that require high accuracy, these methods are not suitable

However, most of these research schemes cannot achieve finer-grained aggregation, cannot meet the flexibility required by smart grids, and have limited application scope. At the same time, they use expensive encryption algorithms and pairing operations, resulting in their computational cost and communication overhead. , data integrity verification, and flexibility etc.

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