On the Expressive Power of Negated Conditions and Negative Authorizations in Access Control Models

2021 ◽  
pp. 102586
Author(s):  
Padmavathi Iyer ◽  
Amirreza Masoumzadeh ◽  
Paliath Narendran
Keyword(s):  
Access Control ◽  
Expressive Power ◽  
Access Control Models ◽  
Control Models

The expressive power of multi-parent creation in monotonic access control models*

1996 ◽  
Vol 4 (2-3) ◽  
pp. 149-165 ◽  
Author(s):  
Paul Ammann ◽  
Ravi S. Sandhu ◽  
Richard Lipton
Keyword(s):  
Access Control ◽  
Expressive Power ◽  
Access Control Models ◽  
Control Models

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

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.

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