Assuring enhanced privacy violation detection model for social networks

Abstract

PurposeVirtually unlimited amounts of data collection by cybersecurity systems put people at risk of having their privacy violated. Social networks like Facebook on the Internet provide an overplus of knowledge concerning their users. Although users relish exchanging data online, only some data are meant to be interpreted by those who see value in it. It is now essential for online social network (OSN) to regulate the privacy of their users on the Internet. This paper aims to propose an efficient privacy violation detection model (EPVDM) for OSN.Design/methodology/approachIn recent months, the prominent position of both industry and academia has been dominated by privateness, its breaches and strategies to dodge privacy violations. Corporations around the world have become aware of the effects of violating privacy and its effect on them and other stakeholders. Once privacy violations are detected, they must be reported to those affected and it's supposed to be mandatory to make them to take the next action. Although there are different approaches to detecting breaches of privacy, most strategies do not have a functioning tool that can show the values of its subject heading. An EPVDM for Facebook, based on a deep neural network, is proposed in this research paper.FindingsThe main aim of EPVDM is to identify and avoid potential privacy breaches on Facebook in the future. Experimental analyses in comparison with major intrusion detection system (IDS) to detect privacy violation show that the proposed methodology is robust, precise and scalable. The chances of breaches or possibilities of privacy violations can be identified very accurately.Originality/valueAll the resultant is compared with well popular methodologies like adaboost (AB), decision tree (DT), linear regression (LR), random forest (RF) and support vector machine (SVM). It's been identified from the analysis that the proposed model outperformed the existing techniques in terms of accuracy (94%), precision (99.1%), recall (92.43%), f-score (95.43%) and violation detection rate (>98.5%).