Abstrait

Impact of Encryption Techniques on Cassification Algorithm for Privacy Preservation of Data

Jharna Chopra, Sampada Satav

In this paper, the Naïve Bayesian and K-Nearest neighbour algorithms have been implemented for classification and AES, Triple DES and Rijndael on nine real-world datasets. The goal of the research is to evaluate the performance of the classification algorithms when the data set is encrypted using a variety of performance metrics: classification accuracy, precision, recall (sensitivity), specificity and lift charts/gain charts and to determine the impact of encryption on these algorithms. We found that aside from the obvious time penalty the implementation of an encryption algorithm to protect user privacy the performance of the classification algorithms remained the same in most of the datasets. However, the time penalties for encrypting the data before it could be used for classification varied greatly depending on the type of algorithm used to encrypt the data.