EFFICIENT INCENTIVE COMPATIBLE MODEL FOR SECURE DATA SHARING

Privacy preserving is one of the most important research topics in the data security field and it has become a serious concern in the secure transformation of personal data in recent years. A number of algorithmic techniques have been designed for Privacy Preserving Data Mining (PPDM). For example, different credit card companies may try to build better data sharing models for credit card fraud detection through PPDA tasks. Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. This raises the question of how to design incentive compatible privacy-preserving data analysis techniques that motivate participating parties to provide truthful input data. A model has been proposed to design the effective incentive compatible model for secure private information transformation. Every user need to transform the private data in many applications (E-Shopping), that must be protected from the hackers. Building this model depends on many privacy preserving data mining techniques like Cryptographic techniques, Privacy preserving association rule and Function Evaluation Theorem. The Incentive model is very efficient for privacy preserving data mining, because it provides the protocols against not only semi-honest adversary model and also the malicious model.