Trust beyond reputation: A computational trust model based on stereotypes

Details Trust beyond reputation: A computational trust model based on stereotypes Trust beyond reputation: A computational trust model based on stereotypes

2011-03-11

arxiv.org/abs/1103.2215v2

Computer Science

Cryptography and Security

Scientific paper

Trust plays important roles in diverse, large scale, open environments. Computational trust models help to, for instance, guide users' judgements in online auction sites about other users; or determine quality of contributions in web 2.0 sites. Most existing trust models, however, require historical information about past behavior of a specific agent in question -- information that is not always available. In contrast, in real life interactions, in order to make the first guess about the trustworthiness of a stranger, we commonly use our "instinct" -- essentially stereotypes developed from our past interactions with "similar" other people. In this paper, we propose StereoTrust, a computational trust model inspired by use of stereotypes in real life. A stereotype contains certain features of agents and an expected outcome of the transaction. These features can be taken from the agents' profile information, or the agents' observed behavior. When facing a stranger, the stereotypes matching stranger's profile are aggregated to derive an expected trust. Additionally, when some information about a stranger's previous transactions is available, StereoTrust uses it to refine the stereotype matching. In case trustor (i.e., the agent that needs to derive trust on other agents) does not have sufficient past transactions, we propose to construct an overlay network to share local knowledge among trustworthy agents. Such extensions of the StereoTrust model thus allows a seamless incorporation of existing techniques based on aggregation of historical data as well as social (web-of-trust) networks.

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