A Topic-Level Privacy Preserving Search in the Medical Field

With the explosive growth of medical information, the users not only query information efficiently and accurately, but also pay attention to the information of sensitivity and privacy. In medical domains, the current privacy preserving methods either use the technology of Access Control List, or need to prepare training documents for each privacy policy. However, it is a time-consuming and impractical way for data owners to assign a privacy policy on each document. In this paper, by exploiting the privacy medical queries, we propose a novel approach based on semantic and ontology to achieve the topic-level privacy preserving search. With the support of them, we first mine all the potential hierarchy and semantics from a user query and acquire sensitive terms relative to privacy policies automatically without training documents.