Affiliations: Department of Computer Science, University of North
Carolina at Charlotte, 9201 University City Blvd, Charlotte, NC 28223-0001, USA.
E-mail: xwu@uncc.edu | Department of Software and Information System,
University of North Carolina at Charlotte, 9201 University City Blvd, Charlotte,
NC 28223-0001, USA. E-mail: yonwang@uncc.edu; sguo@uncc.edu; yzheng@uncc.edu
Abstract: Testing of database applications is of great importance. Although
various studies have been conducted to investigate testing techniques for
database design, relatively few efforts have been made to explicitly address
the testing of database applications which requires a large amount of
representative data available. As testing over live production databases is
often infeasible in many situations due to the high risks of disclosure of
confidential information or incorrect updating of real data, in this paper we
investigate the problem of generating synthetic databases based on a-priori
knowledge about production databases. Our approach is to fit the general
location model using various characteristics (e.g., constraints, statistics,
rules) extracted from a production database and then generate synthetic data
using model learned. The generated data is valid and similar to real data in
terms of statistical distribution, hence it can be used for functional and
performance testing. As characteristics extracted may contain information which
may be used by attackers to derive some confidential information about
individuals, we present our disclosure analysis method which applies cell
suppression technique for identity disclosure and perturbation for value
disclosure analysis.
Keywords: Data Generation, Disclosure Analysis, Statistical Database Modeling
