8,495 research outputs found
A flexible architecture for privacy-aware trust management
In service-oriented systems a constellation of services cooperate, sharing potentially sensitive information and responsibilities. Cooperation is only possible if the different participants trust each other. As trust may depend on many different factors, in a flexible framework for Trust Management (TM) trust must be computed by combining different types of information. In this paper we describe the TAS3 TM framework which integrates independent TM systems into a single trust decision point. The TM framework supports intricate combinations whilst still remaining easily extensible. It also provides a unified trust evaluation interface to the (authorization framework of the) services. We demonstrate the flexibility of the approach by integrating three distinct TM paradigms: reputation-based TM, credential-based TM, and Key Performance Indicator TM. Finally, we discuss privacy concerns in TM systems and the directions to be taken for the definition of a privacy-friendly TM architecture.\u
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
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
Reputation Systems of Online Communities Establishing a Research Agenda
Although online communities make it possible for a far greater number of participants to interact on the Web, there are challenges in creating mechanisms that reveal reputations for participants. Reputation Systems provide a proxy that establishes trust in e-commerce communities, social communities, and social news communities. There remain questions as to how reputation systems can be more widely used in online communities without damaging user confidence because participants have strong privacy expectations. This paper will review reputation systems in online communities, examine types, properties, and issues of reputation systems, survey the use of social networks and reputation systems in popular online communities, and present a research agenda to address issues of reputation systems
A Hybrid Artificial Reputation Model
Agent interaction in a community such as an online buyer-seller scenario is often risky and uncertain. An agent interacts with other agents where initially they know nothing about each other. Currently many reputation models are developed that help consumers select more reputable and reliable service providers. Reputation models also help agents to make a decision on who they should trust and transact with in the future. These reputation models are either built on interaction trust that involves direct experience as a source of information, or they are built upon witness information, also known as word-of-mouth, that involves the reports provided by others. Neither the interaction trust nor the witness information models alone fully succeed in such uncertain interactions. This thesis research introduces the hybrid reputation model combining both interaction trust and witness information to address the shortcomings of existing reputation models when taken separately. Experiments reveal that the hybrid approach leads to better selection of trustworthy agents where consumers select more reputed service providers, eventually lead to more gains by the consumer. Furthermore, the trust model developed is used in calculating trust values of service providers for the case study with a live website ecommerce
A Survey on True-reputation Algorithm for Trustworthy Online Rating System
The average of customer ratings on a product, which we call a reputation, is one of the key factors in online shoping. The common way for customers to express their satisfaction level with their purchases is through online ratings. The overall buyer?s satisfaction is quantified as the aggregated score of all ratings and is available to all buyers. This average score and reputation of a product acts as a guide for online buyers and highly influences consumer?s final purchase decisions. The trustworthiness of a reputation can be achieved when a large number of buyers involved in ratings with honesty. If some users wantedly give unfair ratings to a item, especially when few users have participated, the reputation of the product could easily be modified. In order to improve the trustworthiness of the products in e-commerce sites a new model is proposed with a true - reputation algorithm that repeatedly adjusts the reputation based on the confidence of the user ratings
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