抽象的

Privacy Preserving Data Mining (PPDM) For Horizontally Partitioned Data

Mohasin Tamboli, Jayapal PC Bhalerao M

Due to the increase in sharing sensitive data through networks among businesses, governments and other parties, privacy preserving has become an important issue in data mining and knowledge discovery. Privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. This paper proposes a solution for privately computing data mining classification algorithm for horizontally partitioned data without disclosing any information about the sources or the data. The proposed method (PPDM) combines the advantages of RSA public key cryptosystem and homomorphic encryption scheme. Experimental results show that the PPDM method is robust in terms of privacy, accuracy, and efficiency. Data mining has been a popular research area for more than a decade due to its vast spectrum of applications. However, the popularity and wide availability of data mining tools also raised concerns about the privacy of individuals. The aim of privacy preserving data mining researchers is to develop data mining techniques that could be applied on databases without violating the privacy of individuals. Privacy preserving techniques for various data mining models have been proposed, initially for classification on centralized data then for association rules in distributed environments.