Hatim Alsuwat1, Emad Alsuwat1, Tieming Geng1, Csilla Farkas2, and Chin-TserHuang2, 1University of South Carolina, Columbia SC 29208, USA ,2University of South Carolina, Columbia SC 29208, USA
Shuffling Algorithms have been used to protect the confidentiality of sensitive data.However, shuffling algorithms may not preserve functional dependencies and data driven associations. In this paper, we present two solutions for addressing these shortcomings: (1) Functional dependencies preserving shuffle; (2) Data-driven associations preserving shuffle. For preserving functional dependencies, we propose a method using Boyce-Codd Normal Form (BCNF) decomposition. Instead of shuffling the original relation, we recommend to shuffle each BCNF decomposition. The final shuffled relation is constructed by the natural join of the shuffled decompositions. We show that our approach is lossless and preserves functional dependencies if the BCNF decomposition is dependency preserving. For preserving data-driven association, we generate the transitive closure of the sets of attributes that are associated. Attributes of each set are bundled together during shuffling.
Secure Cryptographic Shuffling Algorithms, Functional Dependencies, Data-drive Association, Database Normalization, Data Confidentiality.