Privacy information protection algorithm of ultra dense network nodes based on edge computing

Author:

Wang Hua1

Affiliation:

1. Research and Development Center, China Academy of Launch Vehicle Technology, Beijing 100076, China

Abstract

In order to solve the problems of high node loss rate, high time overhead and high risk of privacy disclosure in network node privacy information protection, an ultra dense network node privacy information protection algorithm based on edge computing is proposed. The weight update algorithm is used to detect the security vulnerabilities of ultra dense network nodes. According to the detection results, the characteristics of node vulnerabilities are obtained, the sensitive label information of node vulnerabilities is protected through the weighted graph, and the k-anonymity technology is used to anonymize the privacy information of ultra dense network nodes; Finally, edge computing is used to protect the privacy information of nodes. The experimental results show that the node loss rate of the proposed method is always less than 2%, the time overhead is small, and the risk coefficient of privacy disclosure is small.

Publisher

IOS Press

Subject

Artificial Intelligence,Computer Networks and Communications,Software

Reference17 articles.

1. Enzymatic weight update algorithm for DNA-based molecular learning;Baek;Molecules,2019

2. Packet-data anomaly detection in PMU-based state estimator using convolutional neural network;Basumallik;International Journal of Electrical Power & Energy Systems,2018

3. Evaluation of cyber-physical power systems in cascading failure: Node vulnerability and systems connectivity;Chen;IET Generation, Transmission & Distribution,2020

4. OMCPR: Optimal mobility aware cache data pre-fetching and replacement policy using spatial K-anonymity for LBS;Gupta;Wireless Personal Communications,2020

5. Standardization progress and case analysis of edge computing;Huazhang;Journal of Computer Research and Development,2018

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