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access icon free Method for measuring the privacy level of pre-published dataset

  • Author(s): Dan Wang 1 ; Bing Guo 1 ; Yan Shen 2
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  • Source: Volume 12, Issue 5, September 2018, p. 425 – 430
    DOI:  10.1049/iet-ifs.2017.0341 , Print ISSN 1751-8709, Online ISSN 1751-8717
© The Institution of Engineering and Technology
Received 05/07/2017, Accepted 20/04/2018, Revised 28/03/2018, Published 27/04/2018

Several privacy protection technologies have been designed for protecting individuals’ privacy information in data publishing. It is often easy to make additional information loss of a dataset without measuring the strength of privacy protection it required. To apply appropriate strength of privacy preservation, the authors put forward privacy score, a new metric for making a comprehensive evaluation of the privacy information contained in the pre-published dataset. Using this measure, publishers can apply the privacy techniques to the pre-published dataset in accordance with the privacy level it belongs to. The privacy score is determined by the amount as well as the quality of privacy information in which the pre-published dataset is contained. Furthermore, the authors present a data sensitivity model based on analytic hierarchy process for assigning a sensitivity score to each possible value of a sensitive attribute. The reasonability and effectiveness of the proposed approach are verified by using the Adult dataset.

Inspec keywords: data protection; analytic hierarchy process

Other keywords: privacy techniques; information loss; data sensitivity model; privacy score; Adult dataset; analytic hierarchy process; privacy preservation; prepublished dataset; privacy level measurement; data publishing; privacy information; privacy protection technologies

Subjects: Data security

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