Location-aware Privacy and More: A Systems Approach using Context-aware Database Management Systems

摘要

When a user issues a query, database engines will usually return results based solely on the query and the content of the database. However, query issuers have a "context" which if taken into account will certainly change the outcome of the query. Thus, when responding to the query, the database system can consider the query issuer's context and return only the objects/tuples in the database that not only satisfy the query predicates but also are relevant to the query issuer's context. In this paper, we give an overview of Chameleon; a context-aware database management system. Chameleon introduces SQL-level constructs that describe the "context" in which the query is issued as well as the reciprocal contexts of the objects in the database. By tying the query issuer's contexts with the corresponding contexts of the objects in the database, Chameleon can retrieve the objects of relevance to the query context. Using Chameleon's general interfaces for context definition and awareness activation, we show how database systems that offer not only location-sensitive privacy but also other forms of privacy, e.g., both location-sensitive and time-sensitive privacy profiles for their users can be realized easily. Several modeling and performance challenges for realizing context-aware database management systems are presented.