Introduction to Technical Seminar Presentation on Privacy Preserving Data Mining:
Database privacy includes the approaches that are done statistically which includes the altering of frequency of features, and also the erasing of values that are revealed. Query-based approaches: It consists of a trusted third party which is permanent. Perturbation: This is used for adding noise to the query output. The Query monitoring involves disallowing of queries that break privacy.
This paper we deal with the technical feasibility for preserving data mining. The principle of randomization is initiated using Gaussian perturbations. And for the case of decision-tree classification we can have two effective algorithms named By Class and Local. These algorithms are based on Bayesian procedure which is used for correcting the distributions.
The method of privacy preservation includes Value-Class Membership: Here in this method the values of an attribute are divided into as set of mutually exclusive classes. Here the random distributions used are Uniform: it has uniform distribution between [-a, +a ]. And the mean value of the random variable is 0. Gaussian: Here the random variable is distributed over the normal distribution which has mean value m = 0 and a standard deviations.
The implementation of privacy which is given by a method, where we consider a measure which is based on how near are the original values of the attribute which is modified to be estimated.
In this paper we dealt with the technical feasibility for preserving data mining. The principle of randomization is initiated using Gaussian perturbations. And for the case of decision-tree classification we can have two effective algorithms named By Class and Local.
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