scholarly journals Privacy-driven access control in social networks by means of automatic semantic annotation

2016 ◽  
Vol 76 ◽  
pp. 12-25 ◽  
Author(s):  
Malik Imran-Daud ◽  
David Sánchez ◽  
Alexandre Viejo
Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3994
Author(s):  
Yuxi Li ◽  
Fucai Zhou ◽  
Yue Ge ◽  
Zifeng Xu

Focusing on the diversified demands of location privacy in mobile social networks (MSNs), we propose a privacy-enhancing k-nearest neighbors search scheme over MSNs. First, we construct a dual-server architecture that incorporates location privacy and fine-grained access control. Under the above architecture, we design a lightweight location encryption algorithm to achieve a minimal cost to the user. We also propose a location re-encryption protocol and an encrypted location search protocol based on secure multi-party computation and homomorphic encryption mechanism, which achieve accurate and secure k-nearest friends retrieval. Moreover, to satisfy fine-grained access control requirements, we propose a dynamic friends management mechanism based on public-key broadcast encryption. It enables users to grant/revoke others’ search right without updating their friends’ keys, realizing constant-time authentication. Security analysis shows that the proposed scheme satisfies adaptive L-semantic security and revocation security under a random oracle model. In terms of performance, compared with the related works with single server architecture, the proposed scheme reduces the leakage of the location information, search pattern and the user–server communication cost. Our results show that a decentralized and end-to-end encrypted k-nearest neighbors search over MSNs is not only possible in theory, but also feasible in real-world MSNs collaboration deployment with resource-constrained mobile devices and highly iterative location update demands.


2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan

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.


2017 ◽  
Vol 77 (14) ◽  
pp. 18163-18185 ◽  
Author(s):  
Xiaoxia Hu ◽  
Donghui Hu ◽  
Shuli Zheng ◽  
Wangwang Li ◽  
Fan Chen ◽  
...  

2014 ◽  
pp. 451-484
Author(s):  
Rula Sayaf ◽  
Dave Clarke

Access control is one of the crucial aspects in information systems security. Authorizing access to resources is a fundamental process to limit potential privacy violations and protect users. The nature of personal data in online social networks (OSNs) requires a high-level of security and privacy protection. Recently, OSN-specific access control models (ACMs) have been proposed to address the particular structure, functionality and the underlying privacy issues of OSNs. In this survey chapter, the essential aspects of access control and review the fundamental classical ACMs are introduced. The specific OSNs features and review the main categories of OSN-specific ACMs are highlighted. Within each category, the most prominent ACMs and their underlying mechanisms that contribute enhancing privacy of OSNs are surveyed. Toward the end, more advanced issues of access control in OSNs are discussed. Throughout the discussion, different models and highlight open problems are contrasted. Based on these problems, the chapter is concluded by proposing requirements for future ACMs.


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