초록

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Abstract ▼ AI-Helper

Data streams which contain private information should be utilized after the anonymization process. Existing anonymization methods for data streams utilize the manner which stores a certain amount of data and then anonymizes them. These methods suffer from no guarantee of real-time publishing and gen...

Data streams which contain private information should be utilized after the anonymization process. Existing anonymization methods for data streams utilize the manner which stores a certain amount of data and then anonymizes them. These methods suffer from no guarantee of real-time publishing and generate huge information loss. To solve the defects of existing studies, in this paper, we propose a delay-free anonymization method. The delay-free anonymization guarantees real-time application by publishing records immediately using the Anatomy approach just after the input. It also shows high utility by managing the information of published records. Several performance improvement techniques highly decrease the information loss generated during the anonymization process. To our best knowledge, the delay-free anonymization is the first method for guaranteeing the real-time property of data streams. We demonstrate that the proposed anonymization achieves high utility by experiments.

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