A robust trust inference algorithm in weighted signed social networks based on collaborative filtering and agreement as a similarity metric

Akilal, Karim; Slimani, Hachem; Omar, Mawloud

doi:10.1016/j.jnca.2018.11.008

Cited by 12 publications

(12 citation statements)

References 43 publications

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“…In [15], a collaborative filtering approach is used to calculate trust based on the rank that each node has received so far. In using the collaborative filtering method, one needs to consider a similarity measure.…”

Section: Related Workmentioning

confidence: 99%

A community-based distrust-aware computational trust model for online social networks

Kondori

Ashtiani

2022

Preprint

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Trust calculation is an important aspect of social networks research which acts as the main pillar of the network’s existence. The research conducted in this domain still faces unsolved challenges that should be tackled to improve the accuracy of the results. One of these challenges is the cold start problem which we aim to reduce. We have achieved this goal by reducing the dependency of the trust calculation metrics on the links between two individual nodes. We calculate the trust score toward a node without the existence of a link between the trustor and a trustee according to individual characteristics and similarity factors. The consideration of both trust and distrust criteria in the model has resulted in higher accuracy and precision. The network is transformed into communities with nodes having similar subject interests by using topic modeling. The accuracy of the model presented in this research is not reduced despite the low number of communication edges. It has been shown in the evaluations that the mean absolute error of the trust calculations is about 0.01 even with the reduction of 90% of the edges. Hence, the model presented in this research is robust against the edge sparsity of the network. The proposed model is compared with notable current works namely the GCR method and the method presented by Akilal et al. Based on the average absolute error criterion, with a reduction of 90% of the edges, the accuracy of the proposed model shows a 98% increase compared to the GCR method and 96% increase compared to the Akilal et al.’s proposed method.

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Section: Related Workmentioning

confidence: 99%

A community-based distrust-aware computational trust model for online social networks

Kondori

Ashtiani

2022

Preprint

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“…When a user involves data items from multiple users, the trust value among users can be used to weigh the weight of user opinions to determine whether the data items are released or not, thus enabling collaborative privacy management [27]. In addition, a series of studies have proposed an unsupervised trust inference algorithm that is based on collaborative filtering in weighted social networks and a fast and robust trust inference algorithm [28,29] to strengthen the security of social networks via trust inference and to satisfy the goal of differential privacy, a privacy and availability data clustering (PADC) scheme based on k -means algorithm and differential privacy is proposed, which can enhance the selection of the initial center points and the distance calculation method from other points to the center point [30].…”

Section: Protection On the Privacy Of Friendshipsmentioning

confidence: 99%

Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Tian

2021

Security and Communication Networks

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Online social networks provide users with services such as online interaction, instant messaging, and information sharing. The friend search engine, a new type of social application, provides users with the service for querying the list of other individuals’ friends. Currently, the existing research focuses on independent attacks for friend search engines while ignoring the more complicated collusion attacks, which can expose more friendships that users are not willing to share. Compared with independent attackers, collusion attackers share query results by cooperating with each other. In this article, we propose a resistance strategy against collusion attacks to protect the friendship privacy. The proposed trust metric is based on users’ behaviors and is combined with Shamir’s secret sharing system, which can transform friendships into secrets. Through secret distribution and reconfiguration, only the participants who meet the query requirements can successfully reconstruct the secret, while the participants who do not meet the query conditions cannot successfully obtain the secret fragments even if they obtain the secret fragments. Experiments are conducted to verify the effectiveness of the proposed strategy and proved that this strategy can greatly limit the number of malicious attackers, greatly reduce the probability of successful collusion attacks, and reduce the number of victims.

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“…This semiring favors arcs with bigger certainty values and circumvents the intransitivity of distrust by simply ignoring paths with two successive negative links. Taking a different approach that does not rely on trust transitivity, Akilal et al (2019) proposed a collaborative filtering based algorithm using agreement as a similarity metric to infer both trust and distrust relations by using only information from the direct neighbors of the trustors and the trustees. In addition to their time complexity (Ghavipour and Meybodi, 2018), and as noted by Jiang et al (2016a), propagative approaches suffer from path dependence, trust decay, and opinion conflict.…”

Section: Local Metrics Trust Predictionmentioning

confidence: 99%

“…Trust by Agreement (AGR) we took the predicted trust value by agreement as described in Akilal et al (2019).…”

Section: Reciprocal (Rec) Is Obviously the Simplest This Algorithm Is Based On The Assumptionmentioning

confidence: 99%

Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks

Akilal

Omar

Slimani

2020

Knowledge-Based Systems

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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A robust trust inference algorithm in weighted signed social networks based on collaborative filtering and agreement as a similarity metric

Cited by 12 publications

References 43 publications

A community-based distrust-aware computational trust model for online social networks

A community-based distrust-aware computational trust model for online social networks

Anticollusion Attack Strategy Combining Trust Metrics and Secret Sharing for Friendships Protection

Characterizing and using gullibility, competence, and reciprocity in a very fast and robust trust and distrust inference algorithm for weighted signed social networks

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