Emendation of Undesirable Attack on Multiparty Data Sharing With Anonymous Id Assignment Using AIDA Algorithm

Security is a state of being free from danger or threat. When someone finds the vulnerabilities and loopholes in a system without permission means the system lacks its security. Wherever a secure data sharing occurs between multiparty there would be the possibility for undesirable attacks. In a variety of application domains such as patient medical records, military applications, social networking, electronic voting, business and personal applications there is a great significance of anonymity. Using this system we can store our data as groups and also encrypt it with encryption key. Only the privileged person can see the data. The secure computation function widely used is secure sum that allows parties to compute the sum of their individual inputs without mentioning the inputs to one another. This function helps to characterize the complexities of the secure multiparty computation. Another algorithm for sharing simple integer data on top of secure sum is built. The sharing algorithm will be used at each iteration of this algorithm for anonymous ID assignment (AIDA). By this algorithm and certain security measures it is possible to have a system which is free from undesirable attacks. Keywords:Vulnerability, anonymity, encryption key, secure multiparty computation, AIDA

An Algorithm Of Anonymous Id Assignment For Secure Data Sharing On A Network

Existing and new algorithms for assigning anonymous IDs are scrutinized with respect to trade-offs among communication and computational requirements. An algorithm for distributed solution of certain polynomials over limited fields improves the scalability of the algorithms. Another form of anonymity as used in secure multiparty computation allows multiple parties on a network to together carry out a global computation that depends on data from each party while the data supposed by each party remains unknown to the other parties. The new algorithms are constructed on top of a secure sum data mining operation using Newton’s identities and Sturm’s theorem. An algorithm for distributed solution of convinced polynomials over limited fields improves the scalability of the algorithms. Markov chain representations are used to find statistics on the number of iterations required and computer algebra gives closed form results for the completion rates.