12 research outputs found
Simultaneous Inference of User Representations and Trust
Inferring trust relations between social media users is critical for a number
of applications wherein users seek credible information. The fact that
available trust relations are scarce and skewed makes trust prediction a
challenging task. To the best of our knowledge, this is the first work on
exploring representation learning for trust prediction. We propose an approach
that uses only a small amount of binary user-user trust relations to
simultaneously learn user embeddings and a model to predict trust between user
pairs. We empirically demonstrate that for trust prediction, our approach
outperforms classifier-based approaches which use state-of-the-art
representation learning methods like DeepWalk and LINE as features. We also
conduct experiments which use embeddings pre-trained with DeepWalk and LINE
each as an input to our model, resulting in further performance improvement.
Experiments with a dataset of 356K user pairs show that the proposed
method can obtain an high F-score of 92.65%.Comment: To appear in the proceedings of ASONAM'17. Please cite that versio
Data centric trust evaluation and prediction framework for IOT
© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas
Three-Way Joins on MapReduce: An Experimental Study
We study three-way joins on MapReduce. Joins are very useful in a multitude
of applications from data integration and traversing social networks, to mining
graphs and automata-based constructions. However, joins are expensive, even for
moderate data sets; we need efficient algorithms to perform distributed
computation of joins using clusters of many machines. MapReduce has become an
increasingly popular distributed computing system and programming paradigm. We
consider a state-of-the-art MapReduce multi-way join algorithm by Afrati and
Ullman and show when it is appropriate for use on very large data sets. By
providing a detailed experimental study, we demonstrate that this algorithm
scales much better than what is suggested by the original paper. However, if
the join result needs to be summarized or aggregated, as opposed to being only
enumerated, then the aggregation step can be integrated into a cascade of
two-way joins, making it more efficient than the other algorithm, and thus
becomes the preferred solution.Comment: 6 page
Data centric trust evaluation and predication framework for IoT
Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas
A New Approach for Trust Prediction by using collaborative filtering based of Pareto dominance in Social Networks
Along with the increasing popularity of social web sites, users rely more on the trustworthiness informationfor many online activities among users.[24] However, such social network data often suffers from two problems,(1)severe data sparsity and are not able to provide users with enough information, (2)dataset’s is very large.Therefore, trust prediction has emerged as an important topic in social network research. In this paper weproposed a new approach by using collaborative filtering method and the concept of Pareto dominance. We usesPareto dominance to perform a pre-filtering process eliminating less representative users from the k-neighbourselection process while retaining the most promising ones. The results from experiments performed on FilmTrustdataset and Epinions dataset
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
IoT trust and reputation: a survey and taxonomy
IoT is one of the fastest-growing technologies and it is estimated that more
than a billion devices would be utilized across the globe by the end of 2030.
To maximize the capability of these connected entities, trust and reputation
among IoT entities is essential. Several trust management models have been
proposed in the IoT environment; however, these schemes have not fully
addressed the IoT devices features, such as devices role, device type and its
dynamic behavior in a smart environment. As a result, traditional trust and
reputation models are insufficient to tackle these characteristics and
uncertainty risks while connecting nodes to the network. Whilst continuous
study has been carried out and various articles suggest promising solutions in
constrained environments, research on trust and reputation is still at its
infancy. In this paper, we carry out a comprehensive literature review on
state-of-the-art research on the trust and reputation of IoT devices and
systems. Specifically, we first propose a new structure, namely a new taxonomy,
to organize the trust and reputation models based on the ways trust is managed.
The proposed taxonomy comprises of traditional trust management-based systems
and artificial intelligence-based systems, and combine both the classes which
encourage the existing schemes to adapt these emerging concepts. This
collaboration between the conventional mathematical and the advanced ML models
result in design schemes that are more robust and efficient. Then we drill down
to compare and analyse the methods and applications of these systems based on
community-accepted performance metrics, e.g. scalability, delay,
cooperativeness and efficiency. Finally, built upon the findings of the
analysis, we identify and discuss open research issues and challenges, and
further speculate and point out future research directions.Comment: 20 pages, 5 Figures, 3 tables, Journal of cloud computin
Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks
Trust can be defined as a measure to determine which source of information is
reliable and with whom we should share or from whom we should accept
information. There are several applications for trust in Online Social Networks
(OSNs), including social spammer detection, fake news detection, retweet
behaviour detection and recommender systems. Trust prediction is the process of
predicting a new trust relation between two users who are not currently
connected. In applications of trust, trust relations among users need to be
predicted. This process faces many challenges, such as the sparsity of
user-specified trust relations, the context-awareness of trust and changes in
trust values over time. In this dissertation, we analyse the state-of-the-art
in pair-wise trust prediction models in OSNs. We discuss three main challenges
in this domain and present novel trust prediction approaches to address them.
We first focus on proposing a low-rank representation of users that
incorporates users' personality traits as additional information. Then, we
propose a set of context-aware trust prediction models. Finally, by considering
the time-dependency of trust relations, we propose a dynamic deep trust
prediction approach. We design and implement five pair-wise trust prediction
approaches and evaluate them with real-world datasets collected from OSNs. The
experimental results demonstrate the effectiveness of our approaches compared
to other state-of-the-art pair-wise trust prediction models.Comment: 158 pages, 20 figures, and 19 tables. This is my PhD thesis in
Macquarie University, Sydney, Australi