Privacy preserving data mining is a hot research field of 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 $∊$ parameter and enables researchers to mine data which includes sensitive information. Implementations of some well-known classification algorithms such as k-NN, Naïve Bayes, ID3, etc. with differential privacy have been developed. Although the success of the rule-based classifiers using meta-heuristics such as Ant-Miner, BeeMiner etc. in data mining has been demonstrated, any implementation of these classification algorithms with differential privacy has not been proposed in the literature until now to our best knowledge. Artificial bee colony (ABC) is a nature inspired algorithm which imitates foraging behavior of bees, and some approaches using ABC to discover classification rules have been proposed recently and the success of ABC algorithm for the discovery of classification rules has been demonstrated. Motivated by this shortcoming in the literature, we propose to develop a rule-based classifier using ABC algorithm with input perturbation technique of differential privacy to perform privacy preserving classification. According to our experimental results, the proposed ABC-based classifier performs better than the well-known algorithms that are SVM, C4.5, Holte’s One Rule, PART, and RIPPER over non-private and differentially private versions of the datasets in terms of classification performance.

隐私保护数据挖掘是数据挖掘的一个热门研究领域。隐私保护数据挖掘的目的是在执行数据挖掘技术时防止个人敏感信息的泄漏。分类任务是数据挖掘中研究最多的领域之一，因此在隐私保护数据挖掘中也是如此。另一方面，差分隐私是一种强大的隐私保障，它通过使用$∊$参数，使研究人员能够挖掘包含敏感信息的数据。已经开发了一些具有差异隐私的著名分类算法的实现，例如 k-NN、朴素贝叶斯、ID3 等。尽管使用元的基于规则的分类器的成功- 数据挖掘中的启发式算法，如 Ant-Miner、BeeMiner 等已经被证明，据我们所知，这些具有差异隐私的分类算法的任何实现直到现在还没有在文献中提出。人工蜂群(Artificial Bee Colony，ABC)是一种模仿蜜蜂觅食行为的自然启发式算法，最近提出了一些利用ABC发现分类规则的方法，证明了ABC算法发现分类规则的成功。受文献中的这一缺点的启发，我们建议开发一种基于规则的分类器，使用 ABC 算法和差分隐私的输入扰动技术来执行隐私保护分类。根据我们的实验结果，