A Trust-Aware System for Personalized User Recommendations in Social Networks

Eirinaki, Magdalini; Louta, Malamati; Varlamis, Iraklis

doi:10.1109/tsmc.2013.2263128

Cited by 101 publications

(57 citation statements)

References 40 publications

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“…Successful applications include mobile ad hoc networks [26], wireless sensor networks [27], social networks [28], multi-agent networks [29] and, the most recent, anonymity networks [30,31]. These successful applications confirm the effectiveness and necessity of trust for network security.…”

Section: Related Workmentioning

confidence: 81%

On the Detection of Fake Certificates via Attribute Correlation

2015

Entropy

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Transport Layer Security (TLS) and its predecessor, SSL, are important cryptographic protocol suites on the Internet. They both implement public key certificates and rely on a group of trusted certificate authorities (i.e., CAs) for peer authentication. Unfortunately, the most recent research reveals that, if any one of the pre-trusted CAs is compromised, fake certificates can be issued to intercept the corresponding SSL/TLS connections. This security vulnerability leads to catastrophic impacts on SSL/TLS-based HTTPS, which is the underlying protocol to provide secure web services for e-commerce, e-mails, etc. To address this problem, we design an attribute dependency-based detection mechanism, called SSLight. SSLight can expose fake certificates by checking whether the certificates contain some attribute dependencies rarely occurring in legitimate samples. We conduct extensive experiments to evaluate SSLight and successfully confirm that SSLight can detect the vast majority of fake certificates issued from any trusted CAs if they are compromised. As a real-world example, we also implement SSLight as a Firefox add-on and examine its capability of exposing existent fake certificates from DigiNotar and Comodo, both of which have made a giant impact around the world.

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Section: Related Workmentioning

confidence: 81%

On the Detection of Fake Certificates via Attribute Correlation

2015

Entropy

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“…PHAT model depends entirely on the interactions and hence computes the trust quotient of a newly published web services are not recommended Eirinaki et al, [4] [5] introduced a model for trust management in online social networks with respect to its reputation mechanism that considers explicit and implicit connections then provides personalised user recommendations to the network members. It measures the trust and its connections that are determined in social networks and indexes personalised ratings of the trust value of the user.…”

Section: Trustworthy Large Scale Social Network Evaluationmentioning

confidence: 99%

Trust Aware System for Social Networks: A Comprehensive Survey

Manasa¹,

Manjula²,

Venugopal³

2017

IJCA

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Social networks are the platform for the users to get connected with other social network users based on their interest and life styles. Existing social networks have millions of users and the data generated by them are huge and it is difficult to differentiate the real users and the fake users. Hence a trust worthy system is recommended for differentiating the real and fake users. Social networking enables users to send friend requests, upload photos and tag their friends and even suggest them the web links based on the interest of the users. The friends recommended, the photos tagged and web links suggested may be a malware or an untrusted activity. Users on social networks are authorised by providing the personal data. This personal raw data is available to all other users online and there is no protection or methods to secure this data from unknown users. Hence to provide a trustworthy system and to enable real users activities a review on different methods to achieve trustworthy social networking systems are examined in this paper.

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“…It has been assumed that UserConn(Uj → Ui, Tk) lies within the [-1, 1] range, where a value close to 1 (1) indicates that the target ui is a friend(enemy) of the evaluator user uj.…”

Section: Phase 2: Reputation Rating Estimationmentioning

confidence: 99%

Privacy-Preserving Profile Matching System for Trust-Aware Personalized User Recommendations in Social Networks

Kulkarni

Vaidya

2016

Proceedings of the International Conference on Data Engineering and Communication Technology

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Trust is becoming a very important part of social network from the security point of view. In the proposed system, a framework is introduced for handling trust in social networks, which is based on reputation mechanism. The reputation mechanism captures the implicit and explicit connections between the network members, analyses the semantics and dynamics of these connections, and provides personalized user recommendations to another network members. Based on the trust semantics, the system will provide the positive recommendations i.e. list of trustworthy users and the negative recommendations i.e. list of untrustworthy users. Along with this, the proposed system provides one more interesting mode i.e. public profile matching that preserves privacy on social networks. This profile matching contributes in reputation ratings required for suggestions of friend list. The main focus is on providing negative recommendations. In order to compute the reputation of each member, we adopt several other properties of trust such as, transitivity, personalization, and context, and draw ideas from sociology axioms. Trust is not perfectly transitive in social networks, in that trust decays along the transition path, but it is generally agreed that it can be communicated between people. Along with trust generation percentile of profile matching is also considered for personal recommendation.

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A Trust-Aware System for Personalized User Recommendations in Social Networks

Cited by 101 publications

References 40 publications

On the Detection of Fake Certificates via Attribute Correlation

On the Detection of Fake Certificates via Attribute Correlation

Trust Aware System for Social Networks: A Comprehensive Survey

Privacy-Preserving Profile Matching System for Trust-Aware Personalized User Recommendations in Social Networks

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