Privacy-Preserving Data Mining (PPDM) Method for Horizontally Partitioned Data
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.
Keywords: privacy preserving,data mining, K nearest neighbor, secure multi party computation
Download Full-Text
ABOUT THE AUTHORS
Mohamed Awad Ouda
P.hD. student
Sameh A. Salem
Member staff of computer and communication department
Ihab A. Ali
Associate Professor in computer and communication department
El-Sayed M. Saad
Professor in computer and communication department
Mohamed Awad Ouda
P.hD. student
Sameh A. Salem
Member staff of computer and communication department
Ihab A. Ali
Associate Professor in computer and communication department
El-Sayed M. Saad
Professor in computer and communication department
