Privacy preserving rule-based classifiers using modified artificial bee colony optimization algorithm

Privacy preserving data mining is a hot research field for data mining. The aim of privacy preserving data mining is to prevent the leakage of the sensitive information of individuals while performing data mining techniques. Classification task is one of the most studied fields in data mining hence in privacy preserving data mining as well. On the other hand, differential privacy is a powerful privacy guarantee that determines privacy leakage ratio by using ϵ parameter and enables researchers to mine data which includes sensitive information. Although the success of the rule-based classifiers using meta-heuristics such as Ant-Miner etc. in data mining has been demonstrated, any implementation of these classification algorithms with differential privacy has not been proposed in the literature to our best knowledge. Motivated by this, implementations of the rule-based classification algorithms by using meta-heuristics with differential privacy are performed in this thesis. According to the experimental results, the proposed rule-based classification algorithms outperform the other classification techniques in the literature for low ϵ parameters (i.e., ϵ=1). Key Words: Differentially Private Rule-Based Classifiers, Artificial Bee Colony Optimization, Privacy Preserving Classification.