Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection
Joint Authors
Aljably, Randa
Tian, Yuan
Al-Rodhaan, Mznah
Source
Security and Communication Networks
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-07-20
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Information Technology and Computer Science
Abstract EN
Nowadays, user’s privacy is a critical matter in multimedia social networks.
However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it.
Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data.
More precisely, access control models could complement machine learning algorithms in the process of privacy preservation.
The models could use further information derived from the user’s profiles to detect anomalous users.
In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models.
Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users.
It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers.
Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.
American Psychological Association (APA)
Aljably, Randa& Tian, Yuan& Al-Rodhaan, Mznah. 2020. Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection. Security and Communication Networks،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1208459
Modern Language Association (MLA)
Aljably, Randa…[et al.]. Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection. Security and Communication Networks No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1208459
American Medical Association (AMA)
Aljably, Randa& Tian, Yuan& Al-Rodhaan, Mznah. Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection. Security and Communication Networks. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1208459
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208459