Privacy protection of textual attributes through a semantic-based masking method

Identifier:  imarina:9298248

Handle: https://hdl.handle.net/20.500.11797/imarina9298248

Authors:  Martinez, Sergio; Sanchez, David; Valls, Aida; Batet, Montserrat

Abstract:
Using microdata provided by statistical agencies has many benefits from the data mining point of view. However, such data often involve sensitive information that can be directly or indirectly related to individuals. An appropriate anonymisation process is needed to minimise the risk of disclosure. Several masking methods have been developed to deal with continuous-scale numerical data or bounded textual values but approaches to tackling the anonymisation of textual values are scarce and shallow. Because of the importance of textual data in the Information Society, in this paper we present a new masking method for anonymising unbounded textual values based on the fusion of records with similar values to form groups of indistinguishable individuals. Since, from the data exploitation point of view, the utility of textual information is closely related to the preservation of its meaning, our method relies on the structured knowledge representation given by ontologies. This domain knowledge is used to guide the masking process towards the merging that best preserves the semantics of the original data. Because textual data typically consist of large and heterogeneous value sets, our method provides a computationally efficient algorithm by relying on several heuristics rather than exhaustive searches. The method is evaluated with real data in a concrete data mining application that involves solving a clustering problem. We also compare the method with more classical approaches that focus on optimising the value distribution of the dataset. Results show that a semantically grounded anonymisation best preserves the utility of data in both the theoretical and the practical setting, and reduces the probability of record linkage. At the same time, it achieves good scalability with regard to the size of input data. (C) 2011 Elsevier B.V. All rights reserved.