Incremental privacy preservation for associative classification

Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but...

Full description

Saved in:
Bibliographic Details
Main Authors: Bowonsak Seisungsittisunti, Juggapong Natwichai
Format: Conference Proceeding
Published: 2018
Subjects:
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=74049135319&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/48926
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Chiang Mai University
Description
Summary:Privacy preserving has become an essential process for any data mining task. Therefore, data transformation to ensure privacy preservation is needed. In this paper, we address a problem of privacy preserving on an incremental-data scenario in which the data need to be transformed are not static, but appended all the time. Our work is based on a well-known data privacy model, i.e. k-Anonymity. Meanwhile the data mining task to be applied to the given dataset is associative classification. As the problem of privacy preserving for data mining has proven as an NP-hard, we propose to study the characteristics of a proven heuristic algorithm in the incremental scenarios theoretically. Subsequently, we propose a few observations which lead to the techniques to reduce the computational complexity for the problem setting in which the outputs remains the same. In addition, we propose a simple algorithm, which is at most as efficient as the polynomial-time heuristic algorithm in the worst case, for the problem. Copyright 2009 ACM.