Introducing the Factor Importance to Trust of Sources and Certainty of Data in Knowledge Processing Systems - A new Approach for Incorporation and Processing

In knowledge processing systems data is gathered from several sources. After some calculating and processing steps are taken in the system, a result is finally computed and may be used for further steps or by other systems. Most of the time the origin and provenance of input data is not verified. Using unverified data can cause inconsistencies in processing and generating output, and could lead to corrupting threats for the system and the environment as a whole. \ \ We propose an approach where several characterizing values in a given environment - trust of source, certainty of data, and importance (of data) in the current processing step - are used to compute new output characteristics of a knowledge processing system. These values represent the trustworthiness and the certainty of the output in multi-step processing systems based on all used sources and input data. We demonstrate the application of our approach on simple and advanced fictitious scenarios as well as on a real world scenario from the agricultural domain

SuperIdentity: fusion of identity across real and cyber domains

Under both benign and malign circumstances, people now manage a spectrum of identities across both real-world and cyber domains. Our belief, however, is that all these instances ultimately track back for an individual to reflect a single ‘SuperIdentity’. This paper outlines the assumptions underpinning the SuperIdentity Project, describing the innovative use of data fusion to incorporate novel real-world and cyber cues into a rich framework appropriate for modern identity. The proposed combinatorial model will support a robust identification or authentication decision, with confidence indexed both by the level of trust in data provenance, and the diagnosticity of the identity factors being used. Additionally, the exploration of correlations between factors may underpin the more intelligent use of identity information so that known information may be used to predict previously hidden information. With modern living supporting the ‘distribution of identity’ across real and cyber domains, and with criminal elements operating in increasingly sophisticated ways in the hinterland between the two, this approach is suggested as a way forwards, and is discussed in terms of its impact on privacy, security, and the detection of threa

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