Security-Aware Database Migration Planning

Abstract

Database migration is an important problem faced by companies dealing with big data. Not only is migration a costly procedure, but it also involves serious security risks as well. For some institutions, the primary focus is on reducing the cost of the migration operation, which manifests itself in application testing. For other institutions, minimizing security risks is the most important goal, especially if the data involved is of a sensitive nature. In the literature, the database migration problem has been studied from a test cost minimization perspective. In this paper, we focus on an orthogonal measure, i.e., security risk minimization. We associate security with the number of shifts needed to complete the migration task. Ideally, we want to complete the migration in as few shifts as possible, so that the risk of data exposure is minimized. In this paper, we provide a formal framework for studying the database migration problem from the perspective of security risk minimization (shift minimization) and establish the computational complexities of several models in the same. For the NP-hard models, we develop memetic algorithms that produce solutions that are within 10% and 7% of the optimal in 95% of the instances under 8 and 82 seconds, respectively.