Enhancing data mining techniques for secured data sharing and privacy preserving on web mining
Snehal K. Dekate, Jayant Adhikari, Sulbha Parate · 2014
The enhancing data techniques are used in the user database for secure their database from other unauthorized user. This technique is useful for privacy preserving; securely share data among N number of parties. And also apply data mining approach on web service. lgorithms for assigning anonymous IDs are examined with respect to threshold between communication and computational requirements. The new algorithms are built on top of a secure sum data mining operation using Newton's identities and Sturm's theorem. An algorithm for distributed solution of certain polynomials over finite fields enhances 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. The popularity of internet as a communication medium whether for personal or business use depends in part on its support for anonymous communication. Businesses also have legitimate reasons to engage in anonymous communication and avoid the consequences of identity revelation. For example, to allow dissemination of summary data without revealing the identity of the entity the underlying data is associated with, or to protect whistle-blower's right to be anonymous and free from political or economic retributions. Each algorithm can be reasonably implemented and each has its advantages. Our use of the Newton identities greatly decreases communication overhead. This can enable the use of a larger number of slots with a consequent reduction in the number of rounds required. The solution of a polynomial can be avoided at some expense by using Sturm's theorem. The development of a result similar to the Sturm's method over a finite field is an enticing possibility.