Privacy Maintenance Collaborative Data Mining - A Practical Approach

Privacy is an important issue in data mining and knowledge discovery due to the increase in sharing of sensitive data through networks among business, governments and other parties. Data collection is a necessary step in data mining process. Due to privacy reasons, collecting data from different parties becomes difficult. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. The challenge behind this paper is that multiple parties should collaboratively conduct data mining without breaching data privacy. The objective of this paper is to provide solutions for privacy-maintaining collaborative data mining problems. In this paper the study of privacy preserving Add and Multiply Exchange Technology is given and proposed as an approach of Add To Multiply protocol based on homomorphic encryption techniques is defined to exchange the data while keeping it private.In this paper Privacy-maintaining Naive Bayesian classification is demonstrated which is one of the data mining tasks. A secure protocol for multiple parties to conduct the desired computation is developed. The solution is distributed, i.e., there is no central, trusted party having access to all the data.