A Semantic-based Approach to Reduce the Reading Time of Privacy Policies

Abstract

Privacy policy is a legal document in which the users are informed about the data practices used by the organizations. Past research indicates that the privacy policies are long and hard to understand. They are also known to have incomplete content. Users are not inclined to read the policy as they have to read long policies to find information about data practices of an organization. The solution that we are proposing in this research is to assist users with finding relevant content to their queries using semantic approach. This thesis presents the development of domain ontology for privacy policies. Natural Language Processing was used to understand the content of the policies and capture vocabulary for the ontology. This vocabulary was further used to build the ontology so that the ontology highlights relevant sentences related to a privacy concern. We validated and evaluated the ontology using different methods: competency questions, data driven, metric based and user evaluation. Results from the evaluation of ontology show that the amount of text to read is significantly reduced as the users have to only read selected text that ranged from 1% to 30% of a privacy policy. The amount of text depended on the query and its associated keywords. This signifies that the time required to read a policy is significantly reduced as the ontology directs user to the right content for a query. This finding was also confirmed by the results of the user study session. The results from the user study session indicated that the users found ontology helpful in finding relevant selected sentences to read as compared to reading the entire policy.iii ACKNOWLEDGEMENT First, I would like to express my gratitude to my advisor Dr. Rozita Dara for her constant support and advice throughout my research. Her vast knowledge, guidance and patience has helped me get through my masters. Next, I would like to thank my coadvisor Dr. Charlie Obimbo for his inputs and continuous support during my research. His encouragement and generous advice has helped me throughout my masters.Additionally, I would like to thank Dhiren Audich for providing the dataset of privacy policies and Curtis Rasmussen for providing the data set with the user concerns. I would also like to thank the participants who took their time to participate in the user study session. Also, I am forever grateful to my parents and my brother for their love, support and encouragement. This would have not been possible without their help. Lastly, I would like to thank my friends and extended family who have helped me during my study in Canada.