Method for protecting privacy of user and resisting to malicious user in Internet of Things on basis of edge computing

Abstract

The invention belongs to the field of information security and discloses a method for protecting privacy of a user and resisting to a malicious user in Internet of Things on the basis of edge computing, comprising the following steps: introducing an SHE homomorphic cryptosystem, on the basis of protecting the privacy of the user, calculating and updating a trust value of the user, and resisting the malicious user and injection of malicious data; and loading a complex ciphertext operation to an edge node by utilizing an edge computing mode, and protecting the privacy of the user, including thesteps of executing a perception task and updating the trust value of a user executing the perception task under cooperation of a target area edge node and a trust management center. The method disclosed by the invention introduces an emerging edge computing mode, better accords with the actual network demand and can estimate reliability of data without leaking the original data, so that resistanceto the malicious user is realized by introducing a trust management mechanism on the basis of protecting privacy of user data.

Description

technical field [0001] The invention belongs to the field of information security, and in particular relates to a method for protecting user privacy and resisting malicious users in the Internet of Things based on edge computing. Background technique [0002] At present, the technology of protecting user privacy and resisting malicious users in the Internet of Things relies on a central cloud server, which requires that all data of Internet of Things devices need to be transmitted to the cloud server for processing. However, with the development of the Internet of Things technology, there are a large number of Internet of Things devices, and the data that needs to be transmitted to the cloud server for processing is increasing, which brings severe challenges to the transmission bandwidth and delay. In addition, protecting user privacy and resisting malicious users are two relatively independent processes. Due to the protection of user privacy, the reliability of data cannot ...

AI Technical Summary

Problems solved by technology

[0008] (1) In the existing technology, the P2P malicious node detection method based on FP-outlier mining does not consider the situation that malicious users send wrong data. Interaction results accuracy, and even cause traffic accidents, endangering personal and property safety

[0009] (2) In the prior art, a crowdsourcing sensing method that protects user privacy in the fog computing mode can only detect whether the user is working according to the established protocol, and cannot process whether the user has uploaded the correct sensing result

[0010] (3) In the prior art, a reliable data fusion method to protect user privacy can only detect whether the provided data is within the defined valid range. Wrong data with a large data gap will affect the final fusion result

However, the reliability of data encrypted by traditional encryption methods cannot be measured, causing malicious attackers to continue to send false perception data without being detected, which in turn has a negative impact on the data fusion process of the Internet of Things

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