Differential privacy trajectory data protection method based on clustering

Abstract

The invention discloses a differential privacy trajectory data protection method based on clustering. The method comprises: adding Laplace noise to count trajectory positions in a class cluster to resist continuous query attacks; secondly, adding radius-limited Laplace noise to the trajectory position data in the class cluster, so that the clustering effect is prevented from being affected by excessive noise; obtaining a noise clustering center of the class cluster according to the noise position data and the noise position count; and finally, defending a non-track position sensitive information attack in the class cluster by utilizing a differential privacy technology. The method has the advantages that the differential privacy technology is applied to trajectory clustering analysis. Forthe position data in each class cluster, Laplace noise is added to the clustering center, and it is avoided that an attacker inquires the specific position data of the user through the adjacent clustering areas. The size of the noise added to the track position is limited, so that the data availability is improved. Laplace noise is added to other information data possibly causing privacy disclosure, and corresponding reasoning association attacks are resisted.

Description

technical field [0001] The invention relates to a clustering-based differential privacy trajectory data protection method, which aims at the privacy leakage problem of trajectory data in cluster analysis, and belongs to the cross field of engineering application and information science. Background technique [0002] With the popularity of location-aware devices, people enjoy convenient location services, and at the same time, more and more trajectory location data are collected by mobile object databases every day without the user's knowledge. Due to the increasingly powerful database system and the continuous reduction of data storage costs, the collection of personal data is not only the work of government departments and statistical departments, such as the financial sector, Internet companies, medical institutions, etc. all hold a large amount of personal data. With the rapid development of big data technology, data mining has made great progress in some researches and a...

AI Technical Summary

Problems solved by technology

At present, the research on combining differential privacy based on clustering is not mature enough, and there are few studies

For existing differential privacy clustering analysis methods, the availability of most clustering results is not robust to noise

[0005] The above algorithms generally have poor clustering effect, poor data availability, or difficulty in resisting complex attacks based on background knowledge.

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