I. LITERATURE SURVEY Privacy Preserving Data Mining (PPDM) was proposed by D. Agrawal and CC Agrawal [1] and Y. Lindell and B. Pinkas [5] simultaneously. To address this problem, researchers have proposed various solutions that fall into two broad categories based on the level of privacy protection they provide. The first category of the Secure Multiparty Computation (SMC) approach provides the highest level of privacy; allows mutually distrustful entities to mine their collective data without revealing anything except what can be inferred from an entity's input and the output of the mining operation alone by Y. Lindell and B. Pinkas in [5], J . Vaidya and C.W.C.lifton in [6]. In principle, any data mining algorithm can be implemented using generic SMC algorithms by O.Goldreich in [7]. However, these algorithms are extraordinarily expensive in practice and impractical for real-world use. To avoid the high computational costs, various more efficient solutions than general-purpose SMC algorithms have been proposed for specific mining tasks. Solutions to build decision trees on horizontally partitioned data were proposed by Y. Lindell and B. Pinkas in [5]. For vertically partitioned data, algorithms have been proposed to address association rule mining by J. Vaidya and CWClifton in [6], k-means clustering by J. Vaidya and C. Clifton in [8], and frequent pattern problems mining by AWC Fu, RCW Wong and K. Wang in [9]. The work of B. Bhattacharjee, N. Abe, K. Goldman, B. Zadrozny, V. R. Chillakuru, M.del Carpio and C. Apte in [10] uses a secure coprocessor to preserve privacy and data mining and analytics collaborative. The second category of the partial information hiding approach trades pr...... middle of paper ...... that the W. K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. coding system. Mamoulis in [24] can be interrupted without using context-specific information. The success of the attacks in [25] is mainly based on the existence of unique, common, and fake objects, defined by W. K. Wong, D. W. Cheung, E. Hung, B. Kao, and N. Mamoulis in [24]; our scheme does not create such elements and the attacks of Y. Lindell and B. Pinkas in [5] are not applicable to our scheme. Tai et al. [9] assumes that the attacker knows the exact frequency of individual elements, similar to us.