Proximity based trust‐advisor using encounters for mobile societies: Analysis of four filters

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 its establishment in mobile networks, with application to ad hoc networks, including delay tolerant (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. The filters are 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 (three 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.infrastructure-less peer-to-peer mobile networks, such as ad hoc, delay tolerant (DTNs) or sensor networks, cooperation, and subsequently trust is imperative to the construction and operation of the network. Selfish and uncooperative nodes can easily disrupt communication. With trust, several potential applications can be enabled including mobile social networking, building groups and communities of interest, localized alert and emergency notification, context-aware and similaritybased networking [1], to name a few.

Towards privacy-preserving RFID-based location-based services

“…SMILE [6] uses Bluetooth communication and standard cryptographic primitives to mimic the behavior of users in existing missed-connections services such as Craigslist. In [12] the authors constructed a flexible framework for secure mobile social networks, [13] addressed the issue of trust advisory and establishment in mobile networks.…”

A mobile and portable trusted computing platform

“…This problem has attracted the attention of research communities [8][9][10][11][12][13]. The trusted computing group (TCG) [8] has proposed a trusted computing platform (TCP) based on the trusted platform module (TPM) [14] cryptographic microcontroller system.…”

Environmental context aware trust in mobile P2P networks

“…These three matrices are orthogonal to each other and can be combined with different weights according to different requirements. In the state of the art, there are a number of various proposals primarily aimed at evaluating QoS trust [1], with some proposals addressing social and similarity trust [20], [21].…”

“…State of the art addresses this by a) allowing a user to name his/her social friends manually [20], b) by importing social friendships from social networks or e.g. from a phonebook [15] and considering transitivity of trust through friendship chains [7], and c) by finding familiar people by looking at the encounter times or similarity scores [21]. The first two approaches need the user's manual intervention.…”

“…A co-location means that two peers were in the same location (not necessarily at the same time), but may not have interacted, and the amount of time two peers were simultaneously colocated (co-located at the same time) is identified as the encounter time. However, in the latter approach, the proposed solutions to date [20], [21] determine the trust solely based on either the amount of encounter time or the similarity score, i.e. the extent to which two users have been in the same locations.…”