On the Suitability of Applying WordNet to Privacy Measurement

Privacy protection is a fundamental issue in the era of big data. For personalized privacy protection, it is necessary to measure the amount or the degree of privacy leakage. To facilitate such measurement, semantic similarities and relationships of words should be determined since the words may come from multiple sources and present themselves in as many different ways as one can imagine, an intrinsic nature of big data. WordNet has been widely used for measuring the semantic similarity of words. This paper aims at analyzing the suitability of applying WordNet to measuring the semantic similarity or relatedness of words in the field of privacy. The analysis includes an experiment designed to obtain human rating scores as the benchmark dataset and a comprehensive comparison between results from four WordNet based measures and the human rating scores. The conclusion of the analysis is that current WordNet based measures are not very suitable for privacy measurement. Therefore, this paper also provides some suggestions on possible ways of enhancing WordNet to improve the effectiveness of semantic similarity or relatedness measurement in the field of privacy to support more effective and personalized privacy protection.