MSc Defence: Dhiren Audich

 Automating Analysis Of Privacy Policies Through Ontologies: Dhiren Audich

Chair:  Dr. Xining Li
Advisor: Dr. Blair Nonnecke
Co-Advisor:  Dr. Rozita Dara
Advisory Committee Member: Dr. Deborah Stacey
Non-Advisory Committee Member: Dr.  Luiza Antonie

ABSTRACT:

Privacy policies operate as memorandums of understanding (MOUs) between the users and providers of online services. Research suggests that users are deterred from reading policies because of their length, difficult language, and insufficient information. Users are more likely to read short excerpts if they immediately addresses their concerns.

As a first step in helping users find pertinent information in privacy policies, this thesis presents the development of a domain ontology using natural language processing (NLP) algorithms as a way to reduce costs and speed up development.

By using the ontology to locate key parts of privacy policies, average reading times were substantially reduced from 8-12 minutes to 45 seconds. In the process of extracting keywords from the privacy policy corpus, a supervised NLP algorithm performed marginally better (7%) but showed greater promise with larger training sets. Additionally, trained non-domain experts achieved a combined F-score of 71% when compared to a domain expert, and did so when extracting keywords from fewer policies.