CADIVa: cooperative and adaptive decentralized identity validation model for social networks

Abstract

Online Social Networks (OSNs) have successfully changed the way people interact. Online interactions among people span geographical boundaries and interweave with different human-life activities. However, current OSNs identification schemes lack guarantees on quantifying the trustworthiness of online identities of users joining them. Therefore, driven from the need to empower users with an identity validation scheme, we introduce a novel model, Cooperative and Adaptive Decentralized Identity Validation CADIVa, that allows OSN users to assign trust levels to whomever they interact with. CADIVa exploits association rule mining approach to extract the identity correlations among profile attributes in every individual community in a social network. CADIVa is a fully decentralized and adaptive model that exploits fully decentralized learning and cooperative approaches not only to preserve users privacy, but also to increase the system reliability and to make it resilient to mono-failure. CADIVa follows the ensemble learning paradigm to preserve users privacy and employs gossip protocols to achieve efficient and low-overhead communication. We provide two different implementation scenarios of CADIVa. Results confirm CADIVa's ability to provide finegrained community-aware identity validation with average improvement up to 36% and 50% compared to the semi-centralized or global approaches, respectively.

Decentralized and Adaptive K-Means Clustering for Non-IID Data Using HyperLogLog Counters

“…Generating a single global model that accumulates all user behaviours might not produce the best model for particular categories of the users. Specifically, global averaging model enforces a bias towards the behavioural patterns provided by the majority of users, while suppressing the patterns of less significant users [20,21].…”

“…These nodes can be allowed to communicate randomly with any other node in the network, which shapes the underlying topology to a random graph [5,12,18,22]. Also, the communication among nodes can be restricted to enforce a specific underlying graph topology, for example the communication can be only allowed for friendship ties in social networks or among geographically co-located IoT devices [1,20,21].…”

Issues of privacy policy conflict in mobile social network

“…[1][2][3][4][5][6][7][8][9] In recent years, the issues on privacy protection in MSN are deeply researched, and many effective privacy-preserving technologies have been developed. The existing researches on MSN privacy protection mainly concentrate on privacy-preserving data publishing, data mining, and access control, [10][11][12][13][14][15][16][17][18][19][20][21][22][23] in which anonymization is the main privacy-preserving technology for social network data release, so that the data released can meet the need of data analysis while user privacy is not compromised; and social network access control techniques mainly focus on designing social network access control model to solve the problem of social network data access authorization. 18,[24][25][26][27][28][29][30][31][32][33][34][35] However, the conflict of privacy protection policies of access control model inevitably occurs.…”

Personalized Privacy Protecting Model in Mobile Social Network

“…The existing researches on mobile social network privacy protection concentrate mainly on privacy preserving data publishing, data mining and access control [Cheng, Park and Shu (2016); Kokciyan and Yolum (2016); Kumar and Kumar (2017); Schlegel, Chow, Huang et al (2017); Soliman, Bahri and Girdzijauskas (2016); Sun, Yu, Kong et al (2014); Such and Criado (2016); Tai, Yu, Yang et al (2011); Thapa, Liao, Li et al (2016); Wang, Srivatsa and Liu (2012); Zou, Chen and Ozsu (2009)], in which anonymization is the main privacy preserving technology for social network data release, so that the data released can meet the need of data analysis while user privacy is not compromised; and social network access control techniques mainly focuse on designing social network access control model to solve the problem of social network data access authorization [Adam, Atluri, Bertino et al (2002); Carminati, Ferrari and Perego (2006); Cirio, Cruz and Tamassia (2007); Jayaraman, Rinard and Tripunitara (2011); Li, Tang and Mao (2009); Ma, Tao, Zhong et al (2016) ; Yuan and Tong (2005)]. However, there is relatively less research work on personalized privacy protection of social network data, so that it increases the risk of privacy disclosure and the complexity of user privacy settings.…”