Improved privacy Protection model for prevention of data over-collection in smart devices

Abstract

In this study, an attempt was made using machine learning algorithm with the user data store in the mobile cloud framework to solve the problem of data over-collection. This was achieved by designing a model using the security risk level of the applications and the corresponding class level of the users on the smartphone that will help in preventing smartphone apps from accessing and collecting users’ private data while still within the permission scope. Users can store information in the cloud environment where the huge numbers of users are involved. We develop a mobile agent simulator to generate data, and determine the security risk level of the apps on users’ data with the class level of the data. The permission model was designed to determine whether the app is granted permission to access user’s data or not. The data was trained with the use of Neural Network. The evaluation metrics used were accuracy and comparison. For accuracy, the algorithm was compared with the existing algorithm. The data analysis showed that there was restriction for apps accessing the users’ data. The model if deployed on the smartphone will prevent apps from over collect users’ data even while still within the permission scope. This study proved that neural network with mobile cloud computing can be applied to prevent data over-collection in smart devices.