Privacy-preserving Data Splitting: A Combinatorial Approach
This work addresses data privacy protection for data storage and processing, but it appears incremental as it builds on existing data splitting concepts with new mathematical approaches.
The authors tackled the problem of privacy-preserving data splitting by formulating it as a combinatorial problem and developed new combinatorial and algebraic techniques, including an optimal method using Gröbner bases and a greedy algorithm for non-minimal solutions.
Privacy-preserving data splitting is a technique that aims to protect data privacy by storing different fragments of data in different locations. In this work we give a new combinatorial formulation to the data splitting problem. We see the data splitting problem as a purely combinatorial problem, in which we have to split data attributes into different fragments in a way that satisfies certain combinatorial properties derived from processing and privacy constraints. Using this formulation, we develop new combinatorial and algebraic techniques to obtain solutions to the data splitting problem. We present an algebraic method which builds an optimal data splitting solution by using Gröbner bases. Since this method is not efficient in general, we also develop a greedy algorithm for finding solutions that are not necessarily minimal sized.