We introduce an auditing framework for determining whether a database system is adhering to its data disclosure policies. Users formulate audit expressions to specify the (sensitive) data subject to disclosure review. An audit component accepts audit expressions and returns all queries (deemed "suspicious") that accessed the specified data during their execution. The overhead of our approach on query processing is small, involving primarily the logging of each query string along with other minor annotations. Database triggers are used to capture updates in a backlog database. At the time of audit, a static analysis phase selects a subset of logged queries for further analysis. These queries are combined and transformed into an SQL audit query, which when run against the backlog database, identifies the suspicious queries efficiently and precisely. We describe the algorithms and data structures used in a DB2-based implementation of this framework. Experimental results reinforce our design choices and show the practicality of the approach.
Using Hippocratic databases, it is possible to create an auditing framework to check if a database is complying with its data disclosure policies REF .
Auditing compliance with a hippocratic database
In VLDB
Computer Science