Privacy preserving Naïve Bayes classification using Bit-string encryption

The construction of classification models is widely used in data mining. There are concerns about the privacy of data owners because of the need to collect data to build models. In this paper, a Naïve Bayes classification model construction plan is presented, which performs the model construction operation with the participation of data owners and without the need to collect the original data. Instead of collecting data, the scheme uses encryption of bit strings from counting without disclosing the original data to perform the process of creating the Naïve Bayes model. This design allows the model to be built with appropriate performance without the need for trust in a third party with a minimum number of encryptions, so that in terms of time complexity, up to 87% improvement in time cost can be observed. In addition, memory consumption has not increased significantly when compared to designs that use encryption operations.