Solving the Cell Suppression Problem on Tabular Data with Linear Constraints
Management Science
On Replacement Strategies in Steady State Evolutionary Algorithms
Evolutionary Computation
Cell suppression problem: A genetic-based approach
Computers and Operations Research
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Statistical Disclosure Control is the collective name for a range of tools that data providers such as government departments use to protect the confidentiality of individuals or organizations. When the published tables contain magnitude data such as turnover or health statistics, the preferred method is to suppress the values of certain cells. Assigning a cost to the information lost by suppressing any given cell creates the "Cell Suppression Problem". This consists of finding the minimum cost solution which meets the confidentiality constraints. Solving this problem simultaneously for all of the sensitive cells in a table is NP-hard and not possible for medium to large sized tables. In this paper, we describe the development of a heuristic tool for this problem which hybridizes Linear Programming (to solve a relaxed version for a single sensitive cell) with a Genetic Algorithm (to seek an order for considering the sensitive cells which minimizes the final cost). Considering a range of real-world and representative "artificial" datasets, we show that the method is able to provide relatively low cost solutions for far larger tables than is possible for the optimal approach to tackle. We show that our genetic approach is able to significantly improve on the initial solutions provided by existing heuristics for cell ordering, and outperforms local search.