Maximizing data utility while preserving privacy through database fragmentation

Efficiently managing databases that balance data privacy with utility is a critical challenge in today’s data-driven landscape. This study addresses the problem of database fragmentation, which involves dividing a database into smaller fragments, each containing a subset of attributes. The primary objective is to strike a balance between safeguarding the confidentiality of sensitive attribute sets and optimizing the database’s utility. Sensitive attribute sets include combinations of attributes that could disclose private information or identify individuals, such as personal quasi-identifiers, necessitating their separation into distinct fragments to reduce the risk of sensitive data exposure. Conversely, utility attribute sets consist of attributes that enhance data usability and query efficiency. Maximizing utility requires grouping attributes from the same utility set into as few fragments as possible. To effectively solve this complex NP-hard problem, A column generation-based solution leveraging a set partitioning formulation is presented. Experimental evaluations on real and synthetic datasets validate the efficiency of the proposed approach, demonstrating its superiority over the state-of-the-art commercial solver, CPLEX.