Efficient and Secure Data Sharing By Applying AES Algorithm with Anonymous Id Assignment

Security is a basic requirement of an organization in the world to keep their information secure from their competitors. Various techniques and algorithms were developed by research in order to achieve secure data sharing. We propose a technique for anonymous sharing of private data between N parties is developed. This technique is used to allocate these node ID numbers ranging from 1 to N and also apply encryption on private data. These assignments are anonymous in that the identities received are unknown to the other members of the group. Animosity between other members is verified in an information theoretic sense when private communication channels are used. This type of serial numbers assignment allows more complex data to be shared and has applications to other problems in privacy preserving data mining, animosity avoidance in communications and distributed database access. The prescribed computations are distributed without using a trusted third party central authority. Existing and new techniques for assigning anonymous IDs are examined with respect to trade-offs between communication and computational requirements. The proposed technique also finds distributed environment with minimal communication among parties and ensures higher degree of privacy with Advanced Encryption Standard (AES). It generates more secured item sets among multiple parties without affecting mining performance and optimal communication among parties with high privacy and zero percentage of data leakage. The new techniques are built on top of a secure sum data mining operation using Newtons identities and Sturms theorem.