A Clustering Approach for Privacy-Preserving in Social Networks

2015 ◽  
pp. 193-204 ◽  
Author(s):  
Rong Wang ◽  
Min Zhang ◽  
Dengguo Feng ◽  
Yanyan Fu
Keyword(s):  
Social Networks ◽  
Privacy Preserving ◽  
Clustering Approach

scholarly journals Identifying Privacy Related Requirements for the Design of Self-Adaptive Privacy Protections Schemes in Social Networks

2021 ◽  
Vol 13 (2) ◽  
pp. 23
Author(s):  
Angeliki Kitsiou ◽  
Eleni Tzortzaki ◽  
Christos Kalloniatis ◽  
Stefanos Gritzalis
Keyword(s):  
Social Networks ◽  
Interdisciplinary Approach ◽  
Privacy Preserving ◽  
Social Characteristics ◽  
Behavioral Models ◽  
Social Landscape ◽  
Technological Context ◽  
Privacy Risks ◽  
Self Adaptive

Social Networks (SNs) bring new types of privacy risks threats for users; which developers should be aware of when designing respective services. Aiming at safeguarding users’ privacy more effectively within SNs, self-adaptive privacy preserving schemes have been developed, considered the importance of users’ social and technological context and specific privacy criteria that should be satisfied. However, under the current self-adaptive privacy approaches, the examination of users’ social landscape interrelated with their privacy perceptions and practices, is not thoroughly considered, especially as far as users’ social attributes concern. This study, aimed at elaborating this examination in depth, in order as to identify the users’ social characteristics and privacy perceptions that can affect self-adaptive privacy design, as well as to indicate self-adaptive privacy related requirements that should be satisfied for users’ protection in SNs. The study was based on an interdisciplinary research instrument, adopting constructs and metrics from both sociological and privacy literature. The results of the survey lead to a pilot taxonomic analysis for self-adaptive privacy within SNs and to the proposal of specific privacy related requirements that should be considered for this domain. For further establishing of our interdisciplinary approach, a case study scenario was formulated, which underlines the importance of the identified self-adaptive privacy related requirements. In this regard, the study provides further insight for the development of the behavioral models that will enhance the optimal design of self-adaptive privacy preserving schemes in SNs, as well as designers to support the principle of PbD from a technical perspective.

Enabling Privacy-Preserving Rule Mining in Decentralized Social Networks

2021 ◽  
Author(s):  
Aidmar Wainakh ◽  
Aleksej Strassheim ◽  
Tim Grube ◽  
Jörg Daubert ◽  
Max Mühlhäuser
Keyword(s):  
Social Networks ◽  
Privacy Preserving ◽  
Rule Mining

scholarly journals Privacy-Preserving Schemes for Ad Hoc Social Networks: A Survey

2017 ◽  
Vol 19 (4) ◽  
pp. 3015-3045 ◽  
Author(s):  
Mohamed Amine Ferrag ◽  
Leandros Maglaras ◽  
Ahmed Ahmim
Keyword(s):  
Social Networks ◽  
Ad Hoc ◽  
Privacy Preserving

scholarly journals BSO-MV: An Optimized Multiview Clustering Approach for Items Recommendation in Social Networks

2021 ◽  
Vol 27 (7) ◽  
pp. 667-692
Author(s):  
Lamia Berkani ◽  
Lylia Betit ◽  
Louiza Belarif
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Data Sets ◽  
Large Scale Data ◽  
Recommendation Algorithms ◽  
Clustering Approach ◽  
Real World Datasets ◽  
Multiview Clustering ◽  
Improving Accuracy

Clustering-based approaches have been demonstrated to be efficient and scalable to large-scale data sets. However, clustering-based recommender systems suffer from relatively low accuracy and coverage. To address these issues, we propose in this article an optimized multiview clustering approach for the recommendation of items in social networks. First, the selection of the initial medoids is optimized using the Bees Swarm optimization algorithm (BSO) in order to generate better partitions (i.e. refining the quality of medoids according to the objective function). Then, the multiview clustering (MV) is applied, where users are iteratively clustered from the views of both rating patterns and social information (i.e. friendships and trust). Finally, a framework is proposed for testing the different alternatives, namely: (1) the standard recommendation algorithms; (2) the clustering-based and the optimized clustering-based recommendation algorithms using BSO; and (3) the MV and the optimized MV (BSO-MV) algorithms. Experimental results conducted on two real-world datasets demonstrate the effectiveness of the proposed BSO-MV algorithm in terms of improving accuracy, as it outperforms the existing related approaches and baselines.

Decentralized privacy preserving services for Online Social Networks

2018 ◽  
Vol 6 ◽  
pp. 18-25 ◽  
Author(s):  
Leila Bahri ◽  
Barbara Carminati ◽  
Elena Ferrari
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Privacy Preserving
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