Bit Transformation Perturbative Masking Technique for Protecting Sensitive Information In Privacy Preserving Data Mining

摘要

The goal of data mining is ascertaining novel and valuable knowledge from data. In many situations, the extracted knowledge is highly confidential and it needs sanitization before giving to data mining researchers and the public in order to address privacy concerns. There have been two types of privacy in data mining. The first type of privacy is that the data is altered so that the mining result will preserve certain privacy. The second type of privacy is that the data is manipulated so that the mining result is not affected or minimally affected. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on data bases without violating the privacy of individuals. Many techniques for privacy preserving data mining have come up over the last decade. Some of them are statistical, cryptographic, randomization methods, k-anonymity model, l-diversity and etc. In this work, we propose a new perturbative masking technique called bit transformation technique for protecting the sensitive information. An experimental result shows that the proposed technique gives the better result compared with the existing micro-aggregation technique.