Indirect Trust is Simple to Establish

Abstract

In systems with multiple potentially deceptive agents, any single agent may have to assess the trustworthiness of other agents in order to decide with which agents to interact. In this context, indirect trust refers to trust established through third-party advice. Since the advisers themselves may be deceptive or unreliable, agents need a mechanism to assess and properly incorporate advice. We evaluate existing state-of-the-art methods for computing indirect trust in numerous simulations, demonstrating that the best ones tend to be of prohibitively large complexity. We propose a new and easy to implement method for computing indirect trust, based on a simple prediction with expert advice strategy as is often used in online learning. This method either competes with or outperforms all tested systems in the vast majority of the settings we simulated, while scaling substantially better. Our results demonstrate that existing systems for computing indirect trust are overly complex; the problem can be solved much more efficiently than the literature suggests.

Using Change Detection to Adapt to Dynamically Changing Trustees

“…Then System A (MAE=0.06) would have more accurate estimates than System B (MAE=0.2), yet System B would have many more positive interactions than System A. Therefore, in addition to measuring MAE, we also apply a utility-based measure, specifically the relative frequency of unsuccessful interactions (RFU) [21].…”

An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems

“…In addition, some references evaluate trustworthiness by taking advantage of recent performances. In the literature [12,24], they achieve trust by evaluating recent performance and historical evaluations. However, trust evaluation might encounter uncertainty deviated from cognitive impairment, inefficient evidence, randomness, and incomplete information [25,26].…”

“…Thus, we need to ensure that the accumulated information is reliable before being used. Therefore, the concept of confidence is adopted to capture the reliability of gathered information [9,12,15,20,24,28,31]. We define confidence as a probability that the trust is applicable, supported by evidence, consistency, credibility, and certainty.…”

“…Credibility is widely applied in distributed MASs to capture confidence. In most circumstances, credibility can be interpreted as a weight indicating how much the agent could be reliable [12]. It is updated according to the provided information and the interactive feedback [12,24,28,29].…”

“…In terms of distinguishing reliable information collected from third-party agents, some assume that the majority of agents are dependable [2]. Some identify dishonest agents through personal direct experience [9] or filter out the unreliable recommendation and discount their evidence by assigning adaptive weights [9][10][11][12]. Although some of them have excellent security features, most assume that service providers have a list of static performance.…”