rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm. (arXiv:2105.08842v2 [cs.LG] UPDATED)

Traditional approaches for data anonymization consider relational data and
textual data independently. We propose rx-anon, an anonymization approach for
heterogeneous semi-structured documents composed of relational and textual
attributes. We map sensitive terms extracted from the text to the structured
data. This allows us to use concepts like k-anonymity to generate a joined,
privacy-preserved version of the heterogeneous data input. We introduce the
concept of redundant sensitive information to consistently anonymize the
heterogeneous data. To control the influence of anonymization over …

algorithm data identification novel