K-anonymity privacy protection method for track data in uncertain environment

Abstract

The invention discloses a k-anonymity privacy protection method for track data in an uncertain environment. The privacy protection method comprises the following steps: dividing a space into same table cells by virtue of a quadtree, representing concepts, attributes, examples and mutual relations of track data through a body formalization description language based on a field background, reasoning and calculating track data of current attack query by virtue of a Jena inference engine and rules, digging tacit knowledge, and constructing a semanteme-space mixed index model in the uncertain environment; and finding out a track with relatively high similarity with the current attack query through a continuous nearest-neighbor query method by combining track behavior characteristics with a space index model so as to acquire an anonymous data set, and generating an optimal track utilization chain by virtue of a heuristic method, so as to obtain an optimal k-anonymity protection route. By virtue of the privacy protection method, the most possible attacking route adopted by an attacker can be found out, a bug with the greatest threat can be located, and therefore, and the effective track anonymity privacy protection can be implemented.

Description

technical field [0001] The invention relates to the field of research and application of mobile object data mining, in particular to an anonymous privacy protection method for track data in an uncertain environment. Background technique [0002] With the development of mobile police terminal equipment and positioning technology, a large amount of mobile trajectory data has been generated. These data are used in law enforcement, intelligence reconnaissance, information management, emergency rescue, command and dispatch and other fields, forming a mobile complex with graph structure. The internet. Mobile police data is sensitive data, and its transmission and internal sharing within the system require necessary data security and privacy protection mechanisms to avoid unauthorized access and use, thereby ensuring data security and legality. However, these terminal devices have to access the public security internal network through an open wireless public network and the inform...

AI Technical Summary

Problems solved by technology

[0006] However, in the actual anonymity process, the above methods do not take into account that when the data is updated by the mobile object database server, due to the time delay or the limitation of technical conditions, there is a certain deviation in the collected data, which affects the effectiveness of privacy protection.

In addition, the uncertainty of information leads to the instability of the dominance relationship of the trajectory anonymous generalization area, and the success of the k-anonymity method largely depends on the density of the road network

To sum up, in the face of actual application scenarios, existing methods are difficult to meet the requirements of uncertain environments in terms of actual effect and efficiency.

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