Semantic Anonymisation of Set-valued Data

It is quite common that companies and organisations 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 anonymisation mechanisms have been developed up to present, even though most of them focus on structured/relational databases containing numerical or categorical data. However, the anonymisation 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 generalisation of original values; however, this suffers from a high information loss derived from the reduced granularity of the output values. To tackle this problem, in this paper we adapt a well-known microaggregation anonymisation mechanism so that it can be applied to textual set-valued data. Moreover, since the utility of textual data is closely related to their meaning, special care has been put in preserving data semantics. To do so, appropriate 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.