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In today’s digitized environment, information privacy has become a prime concern for everybody. The purpose of this paper is to provide an understanding of information privacy concern arising because of the application of computer-based information system in the various domains (E-Governance, E-Commerce, E-Health, E-Banking and E-Finance), and at different levels, i.e. individual, group, organizational and societal.

The authors performed an in-depth analysis of different research articles related to information privacy concerns and elements affecting those at certain level of applications. The primary sources of literature were articles retrieved from online databases. Various online journal and scholarly articles were searched in detail to locate information privacy-related articles.

The authors have carried out a detailed literature review to identify the different levels where the privacy is a big challenging task. This paper provides insights whether information privacy concern may obstruct in the successful dispersal and adoption of different applications in various application domains. Consumers’ attitude towards information privacy concerns have enlightened and addressed at individual levels in numerous domains. Privacy concerns at the individual level, as suggested by our analysis, seem to have been sufficiently addressed or addressed. However, information privacy concerns at other levels – group, organizational and societal levels – need the attention of researchers.

In this paper, the authors have posited that it will help the researchers to more focus at group level privacy perspective in the information privacy era.

This work presents a novel approach by considering teaching learning based optimization (TLBO) and radial basis function neural networks (RBFNs) for building a classifier for the databases with missing values and irrelevant features. The least square estimator and relief algorithm have been used for imputing the database and evaluating the relevance of features, respectively. The preprocessed dataset is used for developing a classifier based on TLBO trained RBFNs for generating a concise and meaningful description for each class that can be used to classify subsequent instances with no known class label. The method is evaluated extensively through a few bench-mark datasets obtained from UCI repository. The experimental results confirm that our approach can be a promising tool towards constructing a classifier from the databases with missing values and irrelevant attributes.