A survey of trust and reputation systems for online service provision

Trust and reputation systems represent a significant trend in decision support for Internet mediated service provision. The basic idea is to let parties rate each other, for example after the completion of a transaction, and use the aggregated ratings about a given party to derive a trust or reputation score, which can assist other parties in deciding whether or not to transact with that party in the future. A natural side effect is that it also provides an incentive for good behaviour, and therefore tends to have a positive effect on market quality. Reputation systems can be called collaborative sanctioning systems to reflect their collaborative nature, and are related to collaborative filtering systems. Reputation systems are already being used in successful commercial online applications. There is also a rapidly growing literature around trust and reputation systems, but unfortunately this activity is not very coherent. The purpose of this article is to give an overview of existing and proposed systems that can be used to derive measures of trust and reputation for Internet transactions, to analyze the current trends and developments in this area, and to propose a research agenda for trust and reputation systems. Josang, A., R. Ismal, and C. Boyd (2007). A Survey of Trust and Reputation Systems for Online Service Provision, Journal of Decision Support Systems, 43(2):618-644 Citation: Outline of Presentation • Section 1: Introduction • Section 2: Define trust and reputation • Section 3: Trust and Reputation relationship as security mechanisms • Section 4: Collaborative filtering and reputation • Section 5: Trust Classes • Section 6: Four categories of reputation and trust semantics • Section 7: Centralized and distributed architectures for reputation Outline of Presentation Continued • Section 8: Reputation computation methods (Day 1 Goal) • Section 9: Reputation systems used in commercial applications • Section 10: Description of main problems in reputation systems • Section 11: Ending discussion Section 1: Introduction • Online transactions differ from those of in person transactions because of the inherent asymmetry in the transaction, the seller has all the power so to say. • The nature of online transactions obscure the traditional metrics used to establish if a brick and mortar store is trustworthy. Example: a brick and mortar store takes time to establish a web site takes very little time to set up. – These reasons make it hard to determine rather or not a particular online venue is trustworthy or not and is why this trust issue is receiving so much attention from an academic point of view. • The authors of this paper wrote it in part because of the rapidly growing interest in this topic and because they felt that the prior overviews used inconsistent terminology. Section 2: Define trust and reputation • Two kinds of trust: reliability and decision trust • Reliability Trust: Trust is the subjective probability by which an individual, A, expects that another individual, B, performs a given action on which its welfare depends. • Decision Trust: Trust is the extent to which one party is willing to depend on something or somebody in a given situation with a feeling of relative security, even though negative consequences are possible. • The authors mention that the prior mentioned definitions are not as simple as they seem. – For example: Trust in an individual is not necessarily enough to enter into a state of dependence with a person. In other words, that danger might seem to the agent an intolerable risk. • The authors mentions that only a few papers deal with trust and that in economic circles there are some who reject trust as a computational model. • Someone by the name of Williamson argues that the notion of trust should be avoided when modeling economic interactions, because it adds nothing new, and that well known notions such as reliability, utility and risk are adequate ad sufficient for that purpose. • Williamson argues however that personal trust is still important for modeling, and that non-computation models for trust can be meaningful for studying certain relationships. • Concerning reputation, the authors mention two aspects: trust because of good reputation and trust despite of bad reputation. • These two statements shed light on the fact that trust is often made with outside information, knowledge about the relationship that is not general know, instincts and feelings, etc. • Reputation can also be considered as a collective measure of trustworthiness based on referrals from the community. • Research in Trust and Reputation Systems should have two foci: – Finding adequate online substitutes for traditional cues in the physical world and identifying new elements specific to the online applications which are suitable for measurements. – Taking advantage of IT and Internet to create efficient systems for collecting information and deriving measurements of trust and reputation in order to aid decision making and improve online markets. • These simple principles invite rigorous research in order to answer some fundamental questions: What information elements are most suitable for deriving measures of trust and reputation in a given application? How can these information elements be captured and collected? What are the best principles for designing such systems from a theoretic and from a usability point of view? Can they be made resistant to attacks of manipulation by strategic agents? How should users include the information provided by such systems into their decision process? What role can these systems play in the business model of commercial companies? Do these systems truly improve the quality of online trade and interactions? These are important questions that need good answers in order to determine the potential for trust and reputation systems in online environments. • According to a cited reference in the paper, Resnick, a reputation system must have the following: 1. Entities must be long lived, so that with every interaction there is always an expectation of future interactions. 2. Ratings about current interactions are captured and distributed. 3. Ratings about past interactions must guide decisions about current interactions. Example of how trust is derived. (Fig. 1 from paper) Section 3: Trust and Reputation relationship as security mechanisms • In a general sense, the purpose of security mechanisms is to provide protection against malicious parties. • In many situations we have to protect ourselves from those who offer resources so that the problem in fact is reversed. Information providers can for example act deceitfully by providing false or misleading information, and traditional security mechanisms are unable to protect against this type of threat. Trust and reputation systems on the other hand can provide protection against such threats. • To summaries this section the author basically says that a computer system that appears to have robust security appears more trust worthy to the user. Listing known security vulnerabilities and using encryption techniques make the system appear to me more trust worthy. Section 4: Collaborative filtering and reputation • Collaborative filtering systems are a mechanism that shares traits with a reputation system but they are different at the same time. • Collaborative filtering systems (henceforth CF) are a mechanism that attempts to take into consideration that different people have different tastes. • If two separate people rate two items similarly then they are called neighbours in CF terminology. • This new fact can be used to recommend to one something that the other liked, a technique called a recommender system. • This takes the opposite assumption of reputation systems which assume that all people will judge the same performance or transaction consistently. • The example provided by the article is that in CF systems users might rate a video or music file differently based on tastes but one containing a virus would be universally rated poorly. • Another caveat about CF vs reputation systems is that CF systems assume an optimistic world view and reputation systems assume a pessimistic world view. • In specifics, CF systems assume all participants are trustworthy and sincere, meaning that all participants report their genuine opinion. • Conversely, reputation system assume that participants will try to misrepresent the quality of services in order to make more profit and will lie to achieve said goals. • This duel opposing nature of these type systems can make it very advantageous to combine them as will be explored in the study of Amazon in section 9 which does this to some extent. Section 5: Trust Classes • Types of Trust classes: – Provision – Access – Delegation – Identity – Context • Paper mentions them in order to get specific about trust semantics. • Paper focuses on provision trust so it is emphasized. • Provision trust describes the relying party’s trust in a service or resource provider. It is relevant when the relying party is a user seeking protection from malicious or unreliable service providers. I extrapolated from the paper that this is the type of trust that would be studied in business through subjects like contract law. • Access trust describes trust in principals for the purpose of accessing resources owned by or under the responsibility of the relying party. This relates to the access control paradigm which is a central element in computer security. • Delegation trust describes trust in an agent (the delegate) that acts and makes decision on behalf of the relying party. • Identity trust describes the belief that an agent identity is as claimed. Identity trust systems have been discussed mostly in the information security community. An example mentioned in the paper is PGP encryption technology. • Context trust describes the extent to which the relying party believes that the necessary systems and institutions ar