In recent years, data mining has become important player in determining future business strategies. Data mining helps identifying patterns and trends from large amount of data, which can be used for reducing cost, increasing revenue and many more. With increased use of various data mining technologies and larger storage devices, amount of data collected and stored is significantly increased. This data contains personal information like credit card details, contact and residential information, etc. All these reasons have made it inevitable to concentrate on privacy of the data. In order to alleviate privacy concerns, a number of techniques have recently been proposed to perform the data mining in privacy preserving way.
This project briefs about various data mining models and explains in detail about perturbation techniques. Main objective of this project is to achieve two things. First, preserve the accuracy of the data mining models and second, preserve the privacy of the original data. The discussion about transformation invariant data mining models has shown that multiplicative perturbations can theoretically guarantee zero loss of accuracy for a number of models.
Approved and recommended for acceptance as a project in partial fulfillment of the requirements for the degree of Master of Science in Computer and Information Sciences.