MuON: Epidemic Based Mutual Anonymity

Bansod, Neelesh; Malgi, Ashish; Choi, Byung-Hee; Mayo, Jean

doi:10.1109/icnp.2005.26

Cited by 12 publications

(8 citation statements)

References 46 publications

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“…The main limitation is that the group members share some secrecy such as public keys. In contrast, MuON [2] is a mutual anonymity system that uses epidemic-style data dissemination to handle network dynamics in unstructured P2P networks.…”

Section: Related Workmentioning

confidence: 99%

“…By introducing a node liveness predictor q as q = ∆t alive ∆t alive + ∆tsince (2) we have p = q α . p is a monotonically increasing function of q: big values of q mean high probability of being alive.…”

Section: Node Liveness Predictormentioning

confidence: 99%

“…Several observations can be made from this table: (1) lower churn rates increases path durability and routing resilience. (2) Lower churn rates reduce number of attempts in path construction, especially when SimEra uses random mix choice. (3) Biased mix choice enhances routing latencies, because biased mix choice increases path stability, making more paths able to successfully deliver the message segments.…”

Section: Effect Of Churnmentioning

confidence: 99%

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Making Peer-to-Peer Anonymous Routing Resilient to Failures

Zhu

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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Section: Related Workmentioning

confidence: 99%

Section: Node Liveness Predictormentioning

confidence: 99%

Section: Effect Of Churnmentioning

confidence: 99%

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Making Peer-to-Peer Anonymous Routing Resilient to Failures

Zhu

2007

2007 IEEE International Parallel and Distributed Processing Symposium

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“…This paradigm of communication has been proven to reliably deliver the message to all nodes in the network [13] while reducing communication overhead and increasing scalability with respect to traditional broadcast schemes [22], [26]. Due to these advantages, epidemic protocols have been utilized to ensure the mutual anonymity of nodes [4] and also to manage databases [9], routing tables in large peer-to-peer (P2P) overlay networks [28], and the location of resources [29].…”

Section: Introductionmentioning

confidence: 99%

DIMPLE: DynamIc Membership ProtocoL for epidemic protocols

Weber

Choi

Kieckhafer

2007

2007 Fourth International Conference on Broadband Communications, Networks and Systems (BROADNETS '07)

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Abstract-Epidemic protocols assume that information of a random set of nodes is provided at each protocol round. By definition, the random set needs to be chosen uniformly and randomly from the entire set to gossip with. Consequently, a node observes a different set of randomly chosen nodes at each different protocol round. Several proposals have addressed the issue of providing a different random set of nodes at each different round. In general, for large systems, this is done by creating a partial view of the entire membership at each node and exchanging part of the partial view among nodes. While many interesting properties of this approach have been found, investigation is needed to study the performance of this approach with practically high network churn.Without an action specifically designed for churn, shuffling would produce many dangling pointers in the partial view which point to an already left node. This in turn would significantly degrade the quality of epidemic protocols. The reason for the poor quality of the partial view is that the procedures of leave and join can take too long time to handle churn efficiently. To address this issue, an additional action of reinforcement and a new join procedure are proposed and evaluated in this paper. The reinforcement detects and removes dangling pointers at each shuffle more effectively, and the new join procedure accommodates a newly joining node remarkably fast. The subsequent simulations show that these ideas enhance the shuffling mechanism such that the system processes network churn much faster and the quality of the node degrees is significantly enhanced.

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“…Earlier work of MuON was presented previously in [20] and was shown to provide high reliability while maintaining low latencies and low overhead. Here we present an extended version of MuON with enhanced anonymity guarantees and additional performance metrics.…”

Section: Introductionmentioning

confidence: 99%

MuON: Epidemic based mutual anonymity in unstructured P2P networks

et al. 2008

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A mutual anonymity system enables communication between a client and a service provider without revealing their identities. In general, the anonymity guarantees made by the protocol are enhanced when a large number of participants are recruited into the anonymity system. Peer-to-peer (P2P) systems are able to attract a large number of nodes and hence are highly suitable for anonymity systems. However, the churn (changes in system membership) within P2P networks, poses a significant challenge for low-bandwidth reliable anonymous communication in these networks. This paper presents MuON, a protocol to achieve mutual anonymity in unstructured P2P networks. MuON leverages epidemic-style data dissemination to deal with churn. Simulation results and security analysis indicate that MuON provides mutual anonymity in networks with high churn, while maintaining predictable latencies, high reliability, and low communication overhead.

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MuON: Epidemic Based Mutual Anonymity

Abstract: Abstract

Cited by 12 publications

References 46 publications

Making Peer-to-Peer Anonymous Routing Resilient to Failures

Making Peer-to-Peer Anonymous Routing Resilient to Failures

DIMPLE: DynamIc Membership ProtocoL for epidemic protocols

MuON: Epidemic based mutual anonymity in unstructured P2P networks

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