Data collection is being used in all fields nowadays. This data collection of data creates a dataset. Association rule mining is one of the data mining techniques used to extract hidden knowledge within the dataset. These rules are very useful for the organizations but on the other hand, these rules also generate sensitive or confidential information and patterns. Resolving the problem of hiding the sensitive or confidential information with the sensitivity patterns, Privacy preserving data mining (PPDM) is introduced. Privacy preserving data mining is used to hide sensitive and confidential information and patterns, and also preserves the knowledge of the dataset. Various techniques are used to hide such confidential and sensitive information, but they all produce lost rules, ghost rules and hidden failure ratio. In current research work, we propose an algorithm which is based on ant colony optimization (ACO) technique. This proposed methodology is used to triumph over the problem of lost rules, ghost rules and hidden failure ratio. Fuzzy sets are used as the fitness function of ACO. The technique minimizes the problem of lost rule, ghost rule and hidden failure ratio in a great manner. Further-more, this technique is used in all types of areas for hiding sensitive information with sensitive patterns.
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