Abstrakt

Privacy Preserving Data Sharing with Encrypted Anonymous ID Assignment

Akhila M, Nitha L Rozario

An algorithm for sharing of private data among N parties is developed. This work deals with efficient algorithms for assigning identifiers (IDs) to the nodes of a network in such a way that the IDs are anonymous using a distributed computation with no central authority. Given N nodes, this assignment is essentially a permutation of the integers {1….N} with each ID being known only to the node to which it is assigned. Resistance to collusion among other members is verified in an information theoretic sense when private communication channels are used. This assignments of IDs allows complex data to be shared and has applications in privacy preserving data mining, collision avoidance in communications and distributed database access. Existing and new algorithms for assigning IDs are examined with respect to trade-offs between communicational and computational requirements. New algorithms are built on top of secure sum data mining operations using Newtons identities.Markov chain representations are used to find statistics of number of iterations. . In this system, owner is assigned a randomly generated encrypted ID, with which data stored in the database is encrypted. This ensures confidentiality of the data. Another party can access data only if permission is granted by the owner. Also a comparison study based on different encryption methods are performed. The required computations are distributed without using a trusted central authority