Privacy preserving method of spatio-temporal data based on k-generalization technology

doi:10.3969/j.issn.1000-5641.2017.05.016

Abstract

Abstract: In recent years, more and more devices based on location system, resulting in a large amount of location information by the mobile device users to access and use, from the perspective of data mining, the data is of immeasurable value, but in terms of personal privacy, people don't want their information to be leaked and used to sparked strong privacy concerns. At present, many papers have proposed privacy protection technology to solve this problem. Generally speaking, there are several categories of interference, suppression and generalization. In order to protect the privacy of personal spatio-temporal data, this paper proposes a method of k-generalization. To limit the scope of the user may appear, improve the availability of data; selection of nodes to generalization so that the user's maximum security; considers multiple sensitive node solutions exist under the condition, and for the purpose of improving the data utility on a number of sensitive nodes are optimized. Finally, the performance of the algorithm is evaluated by experiments, and it is proved that the algorithm is effective to protect personal privacy.

Key words: privacy, spatio-temporal data, anonymous, generalization

CLC Number:

TP391

YANG Zi, NING Bo, LI Yi. Privacy preserving method of spatio-temporal data based on k-generalization technology[J]. Journal of East China Normal University(Natural Sc, 2017, 2017(5): 174-185.

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