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Traditional approaches for data anonymization consider relational data andtextual data independently. We propose rx-anon, an anonymization approach forheterogeneous semi-structured documents composed of relational and textualattributes. We map sensitive terms extracted from the text to the structureddata. This allows us to use concepts like k-anonymity to generate a joined,privacy-preserved version of the heterogeneous data input. We introduce theconcept of redundant sensitive information to consistently anonymize theheterogeneous data. To control the influence of anonymization over unstructuredtextual data versus structured data attributes, we introduce a modified,parameterized Mondrian algorithm. The parameter $\lambda$ allows to givedifferent weight on the relational and textual attributes during theanonymization process. We evaluate our approach with two real-world datasetsusing a Normalized Certainty Penalty score, adapted to the problem of jointlyanonymizing relational and textual data. The results show that our approach iscapable of reducing information loss by using the tuning parameter to controlthe Mondrian partitioning while guaranteeing k-anonymity for relationalattributes as well as for sensitive terms. As rx-anon is a framework approach,it can be reused and extended by other anonymization algorithms, privacymodels, and textual similarity metrics.

rx-anon -- A Novel Approach on the De-Identification of Heterogeneous Data based on a Modified Mondrian Algorithm