An AI-based causal strategy for securing statistical databases using micro-aggregation

Although Artificial Intelligent (AI) techniques have been used in various applications, their use in maintaining security in Statistical DataBases (SDBs) has not been reported. This paper presents results, that to the best of our knowledge is pioneering, by which concepts from causal networks are used to secure SDBs. We consider the Micro-Aggregation Problem (MAP) in secure SDBs which involves partitioning a set of individual records in a micro-data file into a number of mutually exclusive and exhaustive groups. This problem, which seeks for the best partition of the micro-data file, is known to be NP-hard, and has been tackled using many heuristic solutions. In this paper, we would like to demonstrate that in the process of developing Micro-Aggregation Techniques (MATs), it is expedient to incorporate AI-based causal information about the dependence between the random variables in the micro-data file. This can be achieved by pre-processing the micro-data before invoking any MAT, in order to extract the useful dependence information from the joint probability distribution of the variables in the micro-data file, and then accomplishing the micro-aggregation on the "maximally independent" variables. Our results, on artificial life data sets, show that including such information will enhance the process of determining how many variables are to be used, and which of them should be used in the micro-aggregation process