Data perturbation techniques are one of the most popular models for privacy-preserving data mining due to their practical utility [1]. In a typical data perturbation, before the data owner publishes the data, they randomly change the data in certain way to disguise the private information while preserving some statistical properties for obtaining meaningful data mining models. Agrawal and Harit...

Student, Dept. Of Computer Engineering, Grow More Faculty of Engineering Himatnagar, Gujarat, India Asst. Professor, Dept. of Computer Engineering, Grow More Faculty of Engineering Himatnagar, Gujarat, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract-In recent years, the data mining techniq...

This thesis investigates two new research problems that arise in modern data mining: reasoning on data mining results, and privacy implication of data mining results. Most of the data mining algorithms rely on inductive techniques, trying to infer information that is generalized from the input data. But very often this inductive step on raw data is not enough to answer the user questions, and t...

String data is especially important in the privacy preserving data mining domain because most DNA and biological data is coded as strings. In this paper, we will discuss a new method for privacy preserving mining of string data with the use of simple template based condensation models. The template based model turns out to be effective in practice, and preserves important statistical characteri...

Abstract. Data mining, the extraction of hidden predictive information from large databases, is nothing but discovering hidden value in the data warehouse. Because of the increasing ability to trace and collect large amount of personal information, privacy preserving in data mining applications has become an important concern. Data distortion is one of the well known techniques for privacy pres...

This paper mainly deals with the issue of privacy preserving in data mining while collaborating n number of parties and trying to maintain confidentiality of all data providers details while collaborating their database. Here two type of attacks are addressed “insider attack” and “outsider attack”. In insider attack, the data providers use their own records and try to retrieve other data provid...