A Condensation Approach to Privacy Preserving Data Mining

In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many cases, users are unwilling to provide personal information unless the privacy of sensitive information is guaranteed. In this paper, we propose a new framework for privacy preserving data mining of multi-dimensional data. Previous work for privacy preserving data mining uses a perturbation approach which reconstructs data distributions in order to perform the mining. Such an approach treats each dimension independently and therefore ignores the correlations between the different dimensions. In addition, it requires the development of a new distribution based algorithm for each data mining problem, since it does not use the multi-dimensional records, but uses aggregate distributions of the data as input. This leads to a fundamental re-design of data mining algorithms. In this paper, we will develop a new and ...

• Type: Conference
• Year: 2004
• Where: EDBT
• Authors: Charu C. Aggarwal, Philip S. Yu
• Keywords: Data Mining, Data Mining Algorithms, Database, EDBT 2004, Privacy Preserving Data