A Practical Approach to Network Size Estimation for Structured Overlays

Shafaat, Tallat M.; Ghodsi, Ali; Haridi, Seif

doi:10.1007/978-3-540-92157-8_7

Cited by 21 publications

(14 citation statements)

References 9 publications

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“…The difficulty of this problem strongly depends on the assumptions and features of the network. In fact, if nodes are provided with unique IDs then the task can be accomplished with centralized or hierarchical algorithms in finite time [1]. Differently, if IDs are not present or are not unique to each node then these ID-based strategies cannot be used.…”

Section: Introductionmentioning

confidence: 99%

Distributed Cardinality Estimation in Anonymous Networks

Varagnolo

Pillonetto

Schenato

2014

IEEE Trans. Automat. Contr.

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Abstract-We consider estimation of network cardinality by distributed anonymous strategies relying on statistical inference methods. In particular, we focus on the relative Mean Square Error (MSE) of Maximum Likelihood (ML) estimators based on either the maximum or the average of M -dimensional vectors randomly generated at each node. In the case of continuous probability distributions, we show that the relative MSE achieved by the max-based strategy decreases as 1/M , independently of the used distribution, while that of the average-based estimator scales approximately as 2/M . We then introduce a novel strategy based on the average of M -dimensional vectors locally generated from Bernoulli random variables. In this case, the ML estimator, which is the Least Common Multiple (LCM) of the denominators of the irreducible fractions corresponding to the M elements of the average vector, leads to an MSE which goes exponentially to zero as M increases. We then discuss the effects of finite precision arithmetics in practical dynamic implementations. Numerical experiments reveal that the MSE of the strategy based on Bernoulli trials is two order of magnitude smaller than that based on continuous random variables, at a price of one order of magnitude slower convergence time.

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Section: Introductionmentioning

confidence: 99%

Distributed Cardinality Estimation in Anonymous Networks

Varagnolo

Pillonetto

Schenato

2014

IEEE Trans. Automat. Contr.

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“…To compute its value, every peer should estimate the size of the network at periodic intervals by using any of the approaches proposed in [6,26,27,33]. A new peer can obtain the network size estimates at different instances of time from some of its neighbors in the identity space.…”

Section: Symon Selection Methodsmentioning

confidence: 99%

SyMon: A practical approach to defend large structured P2P systems against Sybil Attack

Jyothi

2010

Peer-to-Peer Netw. Appl.

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Sybil attack is one of the most challenging problems that plague current decentralized Peer-toPeer(P2P) systems. In Sybil attack, a single malicious user creates multiple peer identities known as sybils. These sybils are employed to target honest peers and hence subvert the system. In this paper, we describe a novel solution that enables all honest peers to protect themselves from sybils with high probability in large structured P2P systems. In our proposed sybil defense system, we associate every peer with another non-sybil peer known as SyMon. A given peer's SyMon is chosen dynamically such that the chances of both of them being sybils are very low. The chosen SyMon is entrusted with the responsibility of moderating the transactions involving the given peer and hence makes it almost impossible for sybils to compromise the system. We show the effectiveness of our proposed system in defending against Sybil attack both analytically and experimentally. In addition to this, we explore the feasibility of our proposed solution in two P2P applications: reputation systems for P2P based file sharing applications and P2P applications susceptible to Denial-of-Service(DOS) attack, systems known to be highly vulnerable to Sybil attack. In each of our case studies, we discuss possible ways in which our solution can be employed to defend the system against Sybil attack.

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“…A network size estimation mechanism has been proposed [11]. In this estimation mechanism, each node obtains data for a small part of the structured p2p network, and the node estimates an entire network situation from the local data.…”

Section: Related Workmentioning

confidence: 99%

Accurate Data Aggregation on Unstable Structured P2P Network

Takeda

Oide

Takahashi

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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Smartphones and sensor devices have come into widespread use, and we can observe huge amounts of data from the devices via the Internet. In order to develop a network application which uses these devices effectively, we need an accurate and scalable data aggregation mechanism. In this paper, we propose a accurate data aggregation mechanism which is based on a scalable structured p2p network. The proposed mechanism is constructed by two concepts: Asynchronous Data Aggregation and Time-slot-based Data Aggregation. In the proposed mechanism, the amount of communication data required for data aggregation is O(log N), where N is the number of nodes. Additionally, the proposed mechanism achieves accurate data aggregation even if the structured p2p network is unstable. In this paper, we explain an algorithm and a communication protocol for the proposed data aggregation mechanism. Furthermore, using experimentally obtained results, we indicate advantages of our proposal over existing data aggregation mechanisms.

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A Practical Approach to Network Size Estimation for Structured Overlays

Cited by 21 publications

References 9 publications

Distributed Cardinality Estimation in Anonymous Networks

Distributed Cardinality Estimation in Anonymous Networks

SyMon: A practical approach to defend large structured P2P systems against Sybil Attack

Accurate Data Aggregation on Unstable Structured P2P Network

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