User-defined Privacy Preferences for k-Anonymization in Electronic Crime Reporting Systems for Developing Nations

摘要

Existing approaches that protect data from honest-but-curious data mining service providers include k-anonymity technique, which is considered a better alternative to previously proposed techniques. However k-anonymity technique adopts a generic paradigm approach to privacy enforcement in its model. Owing to the fact that real-life users have different privacy requirements, there is need to address this generic paradigm approach in K-anonymity in order to improve its efficiency. Our proposed approach integrates the concept of a three tier-privacy level (low, medium and high) into k-anonymity to achieve anonymization. This helps us to identify individual users' best choice and how users' privacy preference can be incorporated into the K-anonymity model, as opposed to the generic approach currently adopted. Our preliminary survey presents facts that help to understand factors that influence the choice of users' privacy preference during crime reporting. Results also show that the following factors affect people's privacy choice: Age Group, Personality, Community Need and Cultural Background (Adaptive).