Multi-objective optimization-based privacy in data mining

This paper addresses the data privacy based on interactive computation using an optimization model in data mining. When
data are computed or sharing among users in online, it needs to maintain privacy for all computation during sharing of data.
But user choice-based privacy is not available when sharing of data is required for data mining computation which is a big
challenge for data privacy. Thus, we proposed the framework for anonymity of data privacy using various methods of
multi-objective models as per the requirement of privacy. The proposed framework is designed with the help of two objects
such as computational cost and privacy based on optimization model. Our framework maintains the balance between above
objects as per user demands, i.e., increasing the privacy with decreasing the computational cost. In this model, the domain
of privacy and computational cost for optimization problem solves the entity privacy requirements in a computing
environment. We have used various methods such as Gaussian and uniform distribution, confidence interval, activation
function, linear membership function with distinguish manner for maintaining of privacy and cost. As per the uniform distribution and parameter a-cut value for noise data, the optimal value is made accordingly. Example: for a = 0.2, and uniform distribution (- 1, 1), the optimal value is 0.0058. Similarly, as per different a values, classifiers result is different like a = 0.2 and 0.4, Multilayer perceptron values are 4.01 and 1.61 respectively. The solution of the proposed model controls the amount of privacy with complete freedom of choice of users with utmost flexibility.