What is it about?
Multiple sensitive labels without correlation is a simple scenario in which the data publisher can use any conventional privacy model which supports static data. Our work (SNI) anonymizes a social network having multiple sensitive labels with correlation. Proposed algorithm of SNI minimizes the membership error rate for membership analysis. SNI has been evaluated on cancer label of individual in an Iranian hospital.
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Why is it important?
SNI publishes all labels of vertices without distorting almost with preserving correlation. It calibrates privacy and utility of data, by expert’s desired attack threshold.
Read the Original
This page is a summary of: SNI: Supervised Anonymization Technique to Publish Social Networks Having Multiple Sensitive Labels, Security and Communication Networks, November 2019, Hindawi Publishing Corporation, DOI: 10.1155/2019/8171263.
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