Natural Language Processing and Deep Learning Based Techniques for Evaluation of Companies’ Privacy Policies

Abstract

Companies’ websites are vulnerable to privacy attacks that can compromise the confidentiality of data which, particularly in sensitive use cases like personal data, financial transaction details, medical diagnosis, could be detrimental and unethical. The noncompliance of companies with privacy policies requirements as stipulated by the various Data Protection Regulations has raised lot of concerns for users and other practitioners. To address this issue, previous research developed a model using conventional algorithms such as Neural Network (NN), Logistic Regression (LR) and Support Vector Machine (SVM) to evaluate the levels of compliance of companies to general data protection regulations. However, the research performance shows to be unsatisfactory as the model’s performance across the selected core requirements of the legislation attained F1-score of between 0.52–0.71. This paper improved this model’s performance by using Natural Language Processing (NLP) and Deep Learning (DL) techniques. This was done by evaluating the same dataset used by the previous researcher to train the proposed model. The overall results show that LSTM outperform both GRU and CNN models in terms of F1-score and accuracy. This research paper is to assist the Supervisory Authority and other practitioners to better determine the state of companies’ privacy policies compliance with the relevant data protection regulations.