Bootstrapping opportunistic networks using social roles

Bigwood, Greg; Henderson, Tristan

doi:10.1109/wowmom.2011.5986139

Cited by 22 publications

(8 citation statements)

References 22 publications

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“…There is a range of source obfuscation protocols that have been proposed for opportunistic networks with no infrastructure [6], [18], [21], [31]. They differ in how they determine optimal obfuscator by relying on a range of heuristics based on interest or connectivity similarities.…”

Section: Threat Modelmentioning

confidence: 99%

“…It waits for the tested node to perform the anonymisation and for the "friend"/"trusted" node to forward the result of the obfuscation back to it (line 16). After it receives the response it generates a value for the Reputation status of the tested node based on its opinion of the tested node performance, signs it digitally and stores the updated Reputation value onto the tested node (lines [17][18][19][20].…”

Section: Pseudo Code Of Ocot and Mathematical Notationmentioning

confidence: 99%

“…One of the key reasons for that is that users are inherently reluctant to interact with strangers or third parties, due to privacy concerns of social networking [20]. Work in [18], [22], [31], [34], [6], [21] focuses on how new approaches on improving privacy of users of opportunistic networks by obfuscating their identity, location or adding incentives for good behavior. Newly emerging work [20] proposes a new architecture that addresses the issues of lack of trust, delivery latency, loss of user control, and user privacy by combining the advantages of decentralized storage and opportunistic communications by bridging the gap between the user and the mobile social networking friends who are not in the vicinity of the user.…”

Section: Introductionmentioning

confidence: 99%

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Reputation Aware Obfuscation for Mobile Opportunistic Networks

Radenkovic

Benslimane

McAuley

2015

IEEE Trans. Parallel Distrib. Syst.

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Current anonymity techniques for mobile opportunistic networks typically use obfuscation algorithms to hide node's identity behind other nodes. These algorithms are not well suited to sparse and disconnection prone networks with large number of malicious nodes and new opportunistic, adaptive. So, new, opportunistic, adaptive fully localized mechanisms are needed for improving user anonymity. This paper proposes reputation aware localized adaptive obfuscation for mobile opportunistic networks that comprises of two complementary techniques: opportunistic collaborative testing of nodes' obfuscation behaviour (OCOT) and multidimensional adaptive anonymisation (AA). OCOT-AA is driven by both explicit and implicit reputation building, complex graph connectivity analytics and obfuscation history analyses. We show that OCOT-AA is very efficient in terms of achieving high levels of node identity obfuscation and managing low delays for answering queries between sources and destinations while enabling fast detection and avoidance of malicious nodes typically within the fraction of time within the experiment duration. We perform extensive experiments to compare OCOT-AA with several other competitive and benchmark protocols and show that it outperforms them across a range of metrics over a one month real-life GPS trace. To demonstrate our proposal more clearly, we propose new metrics that include best effort biggest length and diversity of the obfuscation paths, the actual percentage of truly anonymised sources' IDs at the destinations and communication quality of service between source and destination.

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Section: Threat Modelmentioning

confidence: 99%

Section: Pseudo Code Of Ocot and Mathematical Notationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reputation Aware Obfuscation for Mobile Opportunistic Networks

Radenkovic

Benslimane

McAuley

2015

IEEE Trans. Parallel Distrib. Syst.

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“…During an opportunistic encounter, if the two user profiles are friends or share some interests, the users are alerted and can choose to exchange messages. In [2], an opportunistic routing protocol, called Social Role Routing (SRR), that uses OSN information to bootstrap the opportunistic network is presented. SRR categorizes nodes into roles by applying the social network analysis technique of regular equivalence that partitions nodes into classes, where all nodes in a class are connected to the same classes of nodes.…”

Section: Opportunistic Forwarding Using Both Dsn and Osnmentioning

confidence: 99%

“…Although some forwarding schemes [15,19,16,2,7,4,20] using both online social network and detected social network information have been proposed to increase the delivery speed and the likelihood of a message to reach its destination, our approach to exploit more than two social network layers has not been addressed before. We propose a Multi-Layer Social network based Opportunistic Routing (ML-SOR) protocol, in which a node forwards packets using a routing metric that combines three measures: node centrality, tie strength and a tie predictor.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic message routing using multi-layer social networks

Socievole

Yoneki

Rango

et al. 2013

Proceedings of the 2nd ACM Workshop on High Performance Mobile Opportunistic Systems

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In opportunistic networks, the nodes usually exploit a contact opportunity to perform hop-by-hop routing, since an end-to-end path between the source node and destination node may not exist. Most social-based routing protocols use social information extracted from real-world encounter networks to select an appropriate message relay. A protocol based on encounter history, however, takes time to build up a knowledge database from which to take routing decisions. An opportunistic routing protocol which extracts social information from multiple social networks, can be an alternative approach to avoid suboptimal paths due to partial information on encounters. While contact information changes constantly and it takes time to identify strong social ties, online social network ties remain rather stable and can be used to augment available partial contact information. In this paper, we propose a novel opportunistic routing approach, called ML-SOR (Multi-layer Social Network based Routing), which extracts social network information from multiple social contexts. To select an e↵ective forwarding node, ML-SOR measures the forwarding capability of a node when compared to an encountered node in terms of node centrality, tie strength and link prediction. These metrics are computed by ML-SOR on di↵erent social network layers. Trace driven simulations show that ML-SOR, when compared to other schemes, is able to deliver messages with high probability while keeping overhead ratio very small.

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