TY - JOUR
T1 - A Framework for Concrete Reputation-Systems
AU - Krukow, Karl Kristian
AU - Nielsen, Mogens
AU - Sassone, Vladimiro
N1 - Paper id:: http://www.brics.dk/RS/05/23/index.html
PY - 2005
Y1 - 2005
N2 - In a reputation-based trust-management system, agents maintain information about the past behaviour of other agents. This information is used to guide future trust-based decisions about interaction. However, while trust management is a component in security decision-making, few existing reputation-based trust-management systems aim to provide any formal security-guarantees. We provide a mathematical framework for a class of simple reputation-based systems. In these systems, decisions about interaction are taken based on policies that are exact requirements on agents' past histories. We present a basic declarative language, based on pure-past linear temporal logic, intended for writing simple policies. While the basic language is reasonably expressive, we extend it to encompass more practical policies, including several known from the literature. A naturally occurring problem becomes how to efficiently re-evaluate a policy when new behavioural information is available. Efficient algorithms for the basic language are presented and analyzed, and we outline algorithms for the extended languages as well
AB - In a reputation-based trust-management system, agents maintain information about the past behaviour of other agents. This information is used to guide future trust-based decisions about interaction. However, while trust management is a component in security decision-making, few existing reputation-based trust-management systems aim to provide any formal security-guarantees. We provide a mathematical framework for a class of simple reputation-based systems. In these systems, decisions about interaction are taken based on policies that are exact requirements on agents' past histories. We present a basic declarative language, based on pure-past linear temporal logic, intended for writing simple policies. While the basic language is reasonably expressive, we extend it to encompass more practical policies, including several known from the literature. A naturally occurring problem becomes how to efficiently re-evaluate a policy when new behavioural information is available. Efficient algorithms for the basic language are presented and analyzed, and we outline algorithms for the extended languages as well
M3 - Journal article
SN - 0909-0878
SP - 1
EP - 48
JO - B R I C S Report Series
JF - B R I C S Report Series
IS - BRICS RS-05-23
ER -