Evolutionary privacy-preserving data mining
Tzung‐Pei Hong, Kuo-Tung Yang, Jerry Chun‐Wei Lin, Shyue-Liang Wang · World Automation Congress · 2010
Data mining technology can help extract useful knowledge from large data sets. The process of data collection and data dissemination may, however, result in an inherent risk of privacy threats. Some sensitive or private information about individuals, businesses and organizations has to be suppressed before it is shared or published. The privacy-preserving data mining (PPDM) has thus become an important issue in recent years. In this paper, we propose an evolutionary privacy-preserving data mining method to find appropriate transactions to be hidden from a database. The proposed approach designs a flexible evaluation function with three factors, and different weights may be assigned to them depending on users' preference. Besides, the concept of prelarge itemsets is used to reduce the cost of rescanning a database and speed up the evaluation process of chromosomes. The proposed approach can thus easily make a good trade-off between privacy preserving and execution time.