Sensitivity Level-Based Citizen Personal Information Model for Privacy Protection

Abstract

Citizens' privacy concerns have become a major barrier to their acceptance of e-government services, and the question of how to address these privacy issues effectively is increasingly important as government pushes service delivery online. Good protection of citizens' privacy will contribute significantly to the success of e-government services. Therefore, it is highly desirable to take into account privacy issues in e-government service integration to ensure the success of e-government services. On the one hand, there are many challenges in addressing the privacy issues in e-government service integration because users' personal information is often required for consuming e-government services and can potentially be accessed by different types of users (citizens and employees) at various government agencies. In addition, many aspects should be addressed in designing a privacy model. Several solutions have been recently proposed in the literature to deal with privacy concerns. However, there are few practical approaches for helping citizens to create their preferences for privacy protection. Ontology is considered one of the most powerful conceptual approaches to capture knowledge relevant to privacy aspects. Though ontologies for privacy have been suggested, they do not support citizens in setting up their privacy preferences based on various aspects of privacy policy, such as purpose, retention and consent. This paper proposes a new Sensitivity Level-Based Citizen Personal Information Model (SLBCPIM) that can facilitate citizens' role in controlling privacy preferences. This model organises the personal data item of a citizen into a number of sensitivity levels and links these levels with different privacy protection levels to satisfy the citizen's needs. It allows a citizen to set up his or her privacy preferences and supports computerisation of these preferences so that these preferences can be guaranteed. This model has been implemented as an ontology that is machine-readable and can be shared among e-government service systems. A simple Web-based application of this model is developed to validate the usefulness of this new model in supporting citizens in expressing their privacy preferences.