Privacy Preserving Classification Over Encrypted Data using Fully Homomorphic Encryption Technique

Abstract

Applying Machine Learning to a problem which involves medical, financial, or other types of sensitive data needs careful attention in order to maintaining data privacy and security. This paper presents a model for privacy preserving classification and demonstrated that, by using a decision tree classifier, it is possible to perform a privacy preserving classification operation on an encrypted data residing on an untrusted server using the technique of Fully Homomorphic Encryption. First, the paper presented a model for the design and implementation of privacy preserving decision tree classifier over encrypted data. Also, Fully Homomorphic Encryption technique was used to secretly carry out classification on ciphertext using decision tree model built out of confidential medical data. The classifier was implemented using the SEAL homomorphic library and evaluation was done using encrypted medical datasets. The experimental results demonstrated high accuracy of the ciphertext classifier (when compared to the plaintext data equivalent) and efficiency (compared to other classifier on similar tasks). It takes less than 5 seconds (depending on the depth) to perform classification over an encrypted hepatitis feature vector dataset.