Recursive Genetic Micro-Aggregation Technique: Information Loss, Disclosure Risk and Scoring Index

Ebaa Fayyoumi and Omar Alhuniti
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Ebaa Fayyoumi: Department of Computer Science and Applications, Faculty of Prince Al-Hussein Bin Abdallah II for Information Technology, The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Omar Alhuniti: Department of Antiquities, Amman 11118, Jordan

Data, 2021, vol. 6, issue 5, 1-12

Abstract: This research investigates the micro-aggregation problem in secure statistical databases by integrating the divide and conquer concept with a genetic algorithm. This is achieved by recursively dividing a micro-data set into two subsets based on the proximity distance similarity. On each subset the genetic operation “crossover” is performed until the convergence condition is satisfied. The recursion will be terminated if the size of the generated subset is satisfied. Eventually, the genetic operation “mutation” will be performed over all generated subsets that satisfied the variable group size constraint in order to maximize the objective function. Experimentally, the proposed micro-aggregation technique was applied to recommended real-life data sets. Results demonstrated a remarkable reduction in the computational time, which sometimes exceeded 70% compared to the state-of-the-art. Furthermore, a good equilibrium value of the Scoring Index ( S I ) was achieved by involving a linear combination of the General Information Loss ( G I L ) and the General Disclosure Risk ( G D R ) .

Keywords: micro-aggregation techniques; genetic algorithm; secure statistical databases; information loss; disclosure risk (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2021
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