PROTECT: Proximity-based Trust-advisor using Encounters for Mobile Societies

TL;DR

This paper introduces novel proximity-based trust filters for mobile networks, leveraging encounter data to improve trust establishment and network connectivity, with promising results on real-world traces.

Contribution

It proposes four new trust filters based on encounters and evaluates their stability and effectiveness in mobile societies and DTN routing scenarios.

Findings

Trust filters are stable over time with high user overlap.

Different trust filters produce noticeably different results.

Trust filters can mitigate selfishness, improving network connectivity.

Abstract

Many interactions between network users rely on trust, which is becoming particularly important given the security breaches in the Internet today. These problems are further exacerbated by the dynamics in wireless mobile networks. In this paper we address the issue of trust advisory and establishment in mobile networks, with application to ad hoc networks, including DTNs. We utilize encounters in mobile societies in novel ways, noticing that mobility provides opportunities to build proximity, location and similarity based trust. Four new trust advisor filters are introduced - including encounter frequency, duration, behavior vectors and behavior matrices - and evaluated over an extensive set of real-world traces collected from a major university. Two sets of statistical analyses are performed; the first examines the underlying encounter relationships in mobile societies, and the second evaluates DTN routing in mobile peer-to-peer networks using trust and selfishness models. We find that for the analyzed trace, trust filters are stable in terms of growth with time (3 filters have close to 90% overlap of users over a period of 9 weeks) and the results produced by different filters are noticeably different. In our analysis for trust and selfishness model, our trust filters largely undo the effect of selfishness on the unreachability in a network. Thus improving the connectivity in a network with selfish nodes. We hope that our initial promising results open the door for further research on proximity-based trust.