Secured Multiparty Access Control Model for Online Social Networking Using Machine Learning

Abstract

In recent years, Online Social Networks (OSNs) have registered significant growth. They have become part of daily routine life, a phenomenon that received the serious attention of academic, technological, and social research communities. Many OSNs allow its users to post multimedia content, communicate in various ways, and share many aspects of their life in addition to building a virtual network of social relationships. While sharing data to the multiple users, there is no mechanism in place to enforce privacy and security issues in OSNs. Users in OSNs reveal personal information such as their profiles photos, relationship status, phone numbers, dates of birth, and other social activities without being aware of the risks and thefts which may occur. Therefore, this paper aims to capture the concepts of multiparty authorization and policy enforcement in an access control model. The paper deliberates broad-spectrum issues of the multi-users such as privacy and security issues, data integrity, scalability, data authentication, and so on, while sending the data. To overcome such a situation, we need to develop an innovative framework, i.e. Novel Adaptive Privacy Policy Prediction (NA3P), for multi-users access control policies using machine learning classification techniques to provide security and policy prediction for shared content.