Implementation of Efficient Privacy Preserving Data Analysis for Secure Data Transmission

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. The system considers a distributed database such as bank database that is used to construct the incentive model. Basically incentive data are used to check the user knowledge that is the online processing user is correct person or not. 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. Secure multi-party computation (SMC) has recently emerged as an answer to this problem but SMC model they had generate only one key for the each card and after some extent time there may be chance of hacking the password by the hackers. To overcome the above said problem in our proposed system we are using symmetric key cipher algorithm to increase the security measures. In this system it will encrypt the security code so if any hacker check for the key it will be in encrypted format so no one can hack the password. And to send the keys to the mail we are using Java Mail API directly to communicate with the Gmail server.