HMM-Based Trust Model

ElSalamouny, Ehab; Sassone, Vladimiro; Nielsen, Mogens

doi:10.1007/978-3-642-12459-4_3

Cited by 40 publications

(61 citation statements)

References 18 publications

(40 reference statements)

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“…We perform the feature extraction process as described in Section 3. Based on the extracted features, we compare our approach with the outcome-based HMM [6] and the contextual information-based Markov Model [11] on the performance of the rates of correct predictions.…”

Section: Evaluation a Experimental Methodologymentioning

confidence: 99%

“…The Beta model is an early static model in which the behavior of any service provider is assumed to be represented by a fixed probability distribution over outcomes [8]. The Beta model with a decay factor introduces an exponential decay factor to control the weight of each outcome according the time of occurrence [6]. Although this approach shows success in certain scenarios, it is not effective in other scenarios where the provider's behavior is highly dynamic.…”

Section: Introductionmentioning

confidence: 99%

“…ElSalamouny et al [6] uses HMM to predict the outcome of the future transaction based on only the list of outcomes happened in the past achieving better performance than the Beta Model based approaches with a forgetting factor [8]. However, a transaction includes contextual information and an outcome/rating.…”

Section: Introductionmentioning

confidence: 99%

“…The advantage of the Beta reputation system is its flexibility and simplicity as well as its foundation on the theory of statistics. Later on, the Beta model is improved by adding a decay factor to control the weight of data [6], which makes the model focus more on recent data. Zhang and Cohen [26] model agent behaviors with a time window, in which the numbers of successful and failed transactions are aggregated with a forgetting rate and the trustworthiness is adjusted accordingly.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies [6] [14] proposed the HMM approach in modeling transaction results. The model is trained on the outcomes of each past transaction, say failure or success.…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Dynamic Trust of Online Service Providers Using HMM

Zheng

Wang

Orgun

2013

2013 IEEE 20th International Conference on Web Services

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Abstract-Online trading takes place in a very complex environment full of uncertainty in which deceitful service providers or sellers may strategically change their behaviors to maximize their profits. The proliferation of deception cases makes it essential to model the dynamics of a service provider and predict the trustworthiness of the service provider in transactions. Recently, probabilistic trust models have been used to assist decision making in computing environments. Although the typical Hidden Markov Model (HMM) has been used to model a provider's behavior dynamics, existing approaches focus only on the outcomes or ignore the hidden characteristics of the HMM model. In this paper, we model the dynamic trust of service providers concerning a forthcoming transaction in light of as much information as we can consider, including the static features, such as the provider's reputation and item price, and the dynamic features, such as the latest profile changes of a service provider and price changes. Based on a service provider's historical transactions, we predict the trustworthiness of the service provider in a forthcoming transaction. In addition, the Mutual Information theories and the Principle Component Analysis method are leveraged to eliminate redundant information and combine essential features to form lower dimensional feature vectors. Furthermore, by adopting Vector Quantization techniques, we apply the discrete HMM in a more powerful way, in which all the features extracted from both contextual information and the rating of each transaction are treated as observations of HMM. We evaluate our approach empirically in order to study its performance. The experiment results illustrate that our approach significantly outperforms the state-of-theart probabilistic trust methods in accuracy in the cases with complex changes.

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Section: Evaluation a Experimental Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Several studies [6] [14] proposed the HMM approach in modeling transaction results. The model is trained on the outcomes of each past transaction, say failure or success.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Modeling the Dynamic Trust of Online Service Providers Using HMM

Zheng

Wang

Orgun

2013

2013 IEEE 20th International Conference on Web Services

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An Unforeseen Equivalence Between Uncertainty and Entropy

Müller

2019

IFIP Advances in Information and Communication Technology

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Beta models are trust models that use a Bayesian notion of evidence. In that paradigm, evidence comes in the form of observing whether an agent has acted well or not. For Beta models, uncertainty is the inverse of the amount of evidence. Information theory, on the other hand, offers a fundamentally different approach to the issue of lacking knowledge. The entropy of a random variable is determined by the shape of its distribution, not by the evidence used to obtain it. However, we discover that a specific entropy measure (EDRB ) coincides with uncertainty (in the Beta model). EDRB is the expected Kullback-Leibler divergence between two Bernouilli trials with parameters randomly selected from the distribution. EDRB allows us to apply the notion of uncertainty to other distributions that may occur when generalising Beta models.

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Trust in Anonymity Networks

Sassone

Hamadou

Yang

2010

CONCUR 2010 - Concurrency Theory

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Abstract. Anonymity is a security property of paramount importance, as we move steadily towards a wired, online community. Its import touches upon subjects as different as eGovernance, eBusiness and eLeisure, as well as personal freedom of speech in authoritarian societies. Trust metrics are used in anonymity networks to support and enhance reliability in the absence of verifiable identities, and a variety of security attacks currently focus on degrading a user's trustworthiness in the eyes of the other users. In this paper, we analyse the privacy guarantees of the Crowds anonymity protocol, with and without onion forwarding, for standard and adaptive attacks against the trust level of honest users.

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HMM-Based Trust Model

Cited by 40 publications

References 18 publications

Modeling the Dynamic Trust of Online Service Providers Using HMM

Modeling the Dynamic Trust of Online Service Providers Using HMM

An Unforeseen Equivalence Between Uncertainty and Entropy

Trust in Anonymity Networks

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