Effective Incentive Compatible Model for Privacy Preservation of Information in Secure Data Sharing and Publishing

Abstract

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. For example, different credit card companies and disease control centers may try to build better data sharing or publishing models for privacy protection through privacy preserving data mining techniques (PPDM). A model has been proposed to design the effective Privacy Preserving Mining Framework for secure private information transformation and Publishing. Building this framework depends on Incentive Compatible Model based secure code computation process and PPDM techniques like Association rule mining, Randomization method and Cryptographic technique. An Encryption algorithm is used to identify which data sets need to be encrypted for preserving privacy in data storage publishing. The Incentive Compatible model is very efficient in protecting the sensitive data in privacy preserving data sharing, because it provides the secrecy against not only semi-honest adversary model and also the malicious model.