Description
reference paper for this code is :
Omoronyia, Inah, et al. “Engineering adaptive privacy: on the role of privacy awareness requirements.” Proceedings of the 2013 International Conference on Software Engineering. IEEE Press, 2013.
Abstract :
Applications that continuously gather and disclose personal information about users are increasingly common. While disclosing this information may be essential for these applications to function, it may also raise privacy concerns. Partly, this is due to frequently changing context that introduces new privacy threats, and makes it difficult to continuously satisfy privacy requirements. To address this problem, applications may need to adapt in order to manage to change privacy concerns. Thus, we propose a framework that exploits the notion of privacy awareness requirements to identify runtime privacy properties to satisfy. These properties are used to support disclosure decision making by applications. Our evaluations suggest that applications that fail to satisfy privacy awareness requirements cannot regulate users’ information disclosure. We also observe that the satisfaction of privacy awareness requirements is useful to users aiming to minimize exposure to privacy threats, and to users aiming to maximize functional benefits amidst increasing threat severity.
We used Ant Colony Optimization for solving the problem of top paper.
At the first part of the code, the user can determine the location of agent and links.
the output of the first code (case 1):
the output of the first code (case 4):
Train Bayesian neural network by Ant Colony Optimization (ACO) algorithm
Fady Ayman Halaby –
Thanks