Hybrid Approach for Privacy Enhancement in Data Mining Using Arbitrariness and Perturbation  

作　　者：B.Murugeshwari S.Rajalakshmi K.Sudharson 

机构地区：[1]Department of Computer Science and Engineering,Velammal Engineering College,Chennai,600066,India [2]Department of Information Technology,Velammal Institute of Technology,Chennai,601204,India

出　　处：《Computer Systems Science & Engineering》2023年第3期2293-2307,共15页计算机系统科学与工程（英文）

摘　　要：Imagine numerous clients,each with personal data;individual inputs are severely corrupt,and a server only concerns the collective,statistically essential facets of this data.In several data mining methods,privacy has become highly critical.As a result,various privacy-preserving data analysis technologies have emerged.Hence,we use the randomization process to reconstruct composite data attributes accurately.Also,we use privacy measures to estimate how much deception is required to guarantee privacy.There are several viable privacy protections;however,determining which one is the best is still a work in progress.This paper discusses the difficulty of measuring privacy while also offering numerous random sampling procedures and statistical and categorized data results.Further-more,this paper investigates the use of arbitrary nature with perturbations in privacy preservation.According to the research,arbitrary objects(most notably random matrices)have"predicted"frequency patterns.It shows how to recover crucial information from a sample damaged by a random number using an arbi-trary lattice spectral selection strategy.Thisfiltration system's conceptual frame-work posits,and extensive practicalfindings indicate that sparse data distortions preserve relatively modest privacy protection in various situations.As a result,the research framework is efficient and effective in maintaining data privacy and security.

关 键 词：Data mining data privacy ARBITRARINESS data security PERTURBATION 

分 类 号：TP309[自动化与计算机技术—计算机系统结构]