A Study on Privacy Preserving Technology Using Min-Hash Based Similarity Measurement Method

Abstract

Recently, Traditional Methods of measuring the similarity have been time-consuming and costly as the size and area of data increase. Border proposed a Min-Hash efficiently estimates the similarity between two signatures represented two-sets as the connotated form. Min-Hash is widely used in plagiarism prevention, graph and image analysis, and genetic analysis. However, raw data is encrypted but exposure to keys due to frequent use of keys for decryption poses security challenges. In particular, exposure to data about users at large sites such as Facebook and Amazon causes serious damage. More recently, studies of new fourth-generation encryption technologies that can protect user-related data without using the keys needed for encryption have drawn attention. Also, data clustering technology that uses encryption is drawing attention. Thus, among the various clustering methods, this paper presents model using Rushell and Rao similarity for preserving privacy by using RSA homomorphic encryption and estimates efficiently it by using Min-Hash.