Semantic Anonymisation of Set-valued Data

It is quite common that companies and organizations require of releasing and exchanging information related to individuals. Due to the usual sensitive nature of these data, appropriate measures should be applied to reduce the risk of re-identification of individuals while keeping as much data utility as possible. Many anonymization mechanisms have been developed up to present, even though most of them focus on structured/relational databases containing numerical or categorical data. However, the anonymization of transactional data, also known as set-valued data, has received much less attention. The management and transformation of these data presents additional challenges due to their variable cardinality and their usually textual and unbounded nature. Current approaches focusing on set-valued data are based on the generalization of original values; however, this suffers from a high information loss derived from the reduced granularity of output values. To tackle this problem, in this paper we adapt a well-known microaggregation anonymization mechanism so that it can be applied to set-valued data. Moreover, since the utility of textual data is closely related to their meaning, special care has been put in improving the preservation of data semantics. To do so, semantic similarity and aggregation functions are proposed. Experiments conducted on a real set-valued data set show that our proposal better preserves data utility in comparison with non-semantic approaches.

• Publication year

  2014

• Venue

  International Conference on Agents and Artificial Intelligence

• Publication date

  2014-03-06

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.5220/0004811901020112
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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