24,140 research outputs found
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
Talking Nets: A Multi-Agent Connectionist Approach to Communication and Trust between Individuals
A multi-agent connectionist model is proposed that consists of a collection of individual recurrent networks that communicate with each other, and as such is a network of networks. The individual recurrent networks simulate the process of information uptake, integration and memorization within individual agents, while the communication of beliefs and opinions between agents is propagated along connections between the individual networks. A crucial aspect in belief updating based on information from other agents is the trust in the information provided. In the model, trust is determined by the consistency with the receiving agents’ existing beliefs, and results in changes of the connections between individual networks, called trust weights. Thus activation spreading and weight change between individual networks is analogous to standard connectionist processes, although trust weights take a specific function. Specifically, they lead to a selective propagation and thus filtering out of less reliable information, and they implement Grice’s (1975) maxims of quality and quantity in communication. The unique contribution of communicative mechanisms beyond intra-personal processing of individual networks was explored in simulations of key phenomena involving persuasive communication and polarization, lexical acquisition, spreading of stereotypes and rumors, and a lack of sharing unique information in group decisions
Prochlo: Strong Privacy for Analytics in the Crowd
The large-scale monitoring of computer users' software activities has become
commonplace, e.g., for application telemetry, error reporting, or demographic
profiling. This paper describes a principled systems architecture---Encode,
Shuffle, Analyze (ESA)---for performing such monitoring with high utility while
also protecting user privacy. The ESA design, and its Prochlo implementation,
are informed by our practical experiences with an existing, large deployment of
privacy-preserving software monitoring.
(cont.; see the paper
Trust beyond reputation: A computational trust model based on stereotypes
Models of computational trust support users in taking decisions. They are
commonly used to guide users' judgements in online auction sites; or to
determine quality of contributions in Web 2.0 sites. However, most existing
systems require historical information about the past behavior of the specific
agent being judged. In contrast, in real life, to anticipate and to predict a
stranger's actions in absence of the knowledge of such behavioral history, we
often use our "instinct"- essentially stereotypes developed from our past
interactions with other "similar" persons. In this paper, we propose
StereoTrust, a computational trust model inspired by stereotypes as used in
real-life. A stereotype contains certain features of agents and an expected
outcome of the transaction. When facing a stranger, an agent derives its trust
by aggregating stereotypes matching the stranger's profile. Since stereotypes
are formed locally, recommendations stem from the trustor's own personal
experiences and perspective. Historical behavioral information, when available,
can be used to refine the analysis. According to our experiments using
Epinions.com dataset, StereoTrust compares favorably with existing trust models
that use different kinds of information and more complete historical
information
Reputation-based trust evaluations through diversity
Non peer reviewedPostprin
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