Classifying personal data on contextual information

Abstract: In this thesis, a novel approach to classifying personal data is tested. Previous personal data classification models read the personal data before classifying it. However, this thesis instead investigates an approach to classify personal data by looking at contextual information frequently available in data sets. The thesis compares the well-researched word embedding methods Word2Vec, Global representations of Vectors (GloVe) and Bidirectional Encoder Representations from Transformers (BERT) used in conjunction with the different types of classification methods Bag Of Word representation (BOW), Convolutional Neural Networks (CNN), and Long Short-term Memory (LSTM) when solving a personal data classification task. The comparisons are made by extrinsically evaluating the different embeddings' and models' performance in a personal data classification task on a sizable collection of well-labeled datasets belonging to Spotify. The results suggest that the embedded representations of the contextual data capture enough information to be able to classify personal data both when classifying non-personal data against personal data, and also when classifying different types of personal data from each other.