CCE Theses and Dissertations

Date of Award

2020

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Computing and Engineering

Advisor

Michael J. Laszlo

Committee Member

Francisco J. Mitropoulos

Committee Member

Sumitra Mukherjee

Abstract

Microaggregation is a disclosure control method that uses k-anonymity to protect confidentiality in microdata while seeking minimal information loss. The problem is NP-hard. Iterated local search for microaggregation (ILSM) is an effective metaheuristic algorithm that consistently identifies better quality solutions than extant microaggregation methods. The present work presents improvements to local search, the perturbation operations and acceptance criterion within ILSM.

The first, ILSMC, targets changed clusters within local search (LS) to avoid vast numbers of comparison tests, significantly reducing execution times. Second, a new probability distribution yields a better perturbation operator for most cases, significantly reducing the number of iterations needed to find similar quality solutions. A third improves the acceptance criterion by replacing the static balance between intensification and diversification with a dynamic balance. This helps ILSM escape local optima more quickly for some datasets and values of k.

Experimental results with benchmark data show that ILSMC consistently reduces execution times significantly. Targeting changed clusters within LS avoids vast numbers of unproductive tests while allowing search to concentrate on more productive ones. Execution times are decreased by more than an order of magnitude for most benchmark test cases. In the worst case it decreased execution times by 75%. Advantageously, the biggest improvements were with the largest datasets. Perturbing clusters with higher information loss tend to reduce information loss more. Biasing the perturbation operations toward clusters with higher information loss increases the rate of improvement by more than 50 percent in the earliest iterations for two of the benchmarks. Occasionally accepting worse solutions provides diversification; however, increasing the probability of accepting worse solutions closer in quality to the current best solution aids in escaping local optima. This increases the rate of improvement by up to 30 percent in the earliest iterations. Combining the new perturbation operation with the new acceptance criterion can further increase the rate of improvement by as much as 20 percent for some test cases. All three improvements are orthogonal and can be combined for additive effect.

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