Multilevel Privacy Preserving by Linear and Non-Linear Data Distortion

These days privacy preservation topic is based on one of the heated topics of data mining today. With the development of data mining technology, an increasing number of data can be mined out to reveal some potential information about user. While this will lead to a severe problem, which is users' privacy may be violated easily. The goal of privacy preserving is to mine the potential valuable knowledge without leakage of sensitive records, in other words, use non-sensitive data to infer sensitive data. There are many research and branches in this area. Most of them analyze and optimize the technologies and algorithms of privacy preserving data mining. Privacy Preserving Data Mining (PPDM) is used to extract relevant knowledge from large amount of data and at the same time protect the sensitive information from the data miners. The problem in privacy-sensitive domain is solved by the development of the Multi-Level (Multi-Party) Trust Privacy Preserving Data Mining (MLT-PPDM) where multiple differently perturbed copies of the same data is available to data miners at different trusted levels. In MLT-PPDM data owners generate perturbed data by various techniques like Parallel generation, Sequential generation and On-demand generation. MLT-PPDM is robust against the diversity attacks.