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A Model for Corrective Software Maintenance Effort Estimation after Privacy Leak Detection in Social Network

The main aspect that users of social networks are concerned is the privacy of the data is being shared over the social network whose use has become ubiquitous. Most of the research work in this area is concentrated around either privacy preservation or privacy leak detection while the most essential feature of any resilient social network is the ability to handle a privacy breach if one occurs. This ability definitely requires some effort to be expended in order to ensure that the trust of the users is maintained intact. The present paper considers this effort in terms of the software maintenance effort which the software development team has to expend. There are various types of maintenance including adaptive, perfective, predictive and corrective maintenance. The present problem of software maintenance effort estimation after privacy leak detection falls under the category of corrective maintenance; hence, a model for corrective software maintenance effort estimation is presented that is based on architecture of a social network. The main aim of this paper is to ensure that the social network unlearns all the personally identifiable information it has learnt about a user by basing on the framework that can be overlaid on any existing Social Network so that the metrics used for Privacy leak detection and handling are also used for Corrective Software Maintenance Effort Estimation.