Using the Sensitive Mobility Technology of Spark Big Data Platform to Purify the Complex Network Environment of Campus Network
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When the mobility rules in orbit data cross the confidential information of external data, it will bring privacy threat to users who meet the precondition of mobility rules. Traditional research on privacy protection of high-sensitivity mobility rules adopts a method based on the analysis of sensitive mobile networks to eliminate the high-sensitivity knowledge of a single mobility model that cannot effectively resist inference attacks. At the same time, the current privacy protection methods for social networks are limited to identification attacks, and they can not deal with the mobility attacks of sensitive mobile knowledge networks. Therefore, this paper proposes a purification method to deal with inference attacks on sensitive mobile knowledge networks. In this paper, users of communication big data focus on the mobile track data. First of all, this paper studies the method of acquiring mobility knowledge from the data mining of communication track and building a mobility weighted knowledge network with high confidentiality. Secondly, the perceptual component with reasoning attack mode and script based on knowledge network are analyzed. Finally, based on the super big data platform, the algorithm of purifying the mobile mode with high confidentiality in the network, aggravating the network composition, determining the network type, identifying the key nodes, and evaluating the house supply and security is implemented.
Sensitive Mobile Knowledge Network, Complex Network, Data Purification