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https://doi.org/10.1109/INFOCOM.2018.8486394
Title: | Randomized View Reconciliation in Permissionless Distributed Systems | Authors: | I. Jahja H. Yu R. Hou Loi Luu Prateek Saxena |
Issue Date: | 2018 | Publisher: | Institute of Electrical and Electronics Engineers Inc. | Citation: | I. Jahja, H. Yu, R. Hou, Loi Luu, Prateek Saxena (2018). Randomized View Reconciliation in Permissionless Distributed Systems. Proceedings - IEEE INFOCOM 2018-April : 2528 - 2536. ScholarBank@NUS Repository. https://doi.org/10.1109/INFOCOM.2018.8486394 | Abstract: | In a sybil attack, an adversary creates a large number of fake identities/nodes and have them join the system. Computational puzzles have long been investigated as a possible sybil defense: If a node fails to solve the puzzle in a timely fashion, it will no longer be accepted by other nodes. However, it is still possible for a malicious node to behave in such a way that it is accepted by some honest nodes but not other honest nodes. This results in different honest nodes having different views on which set of nodes should form the system. Such view divergence, unfortunately, breaks the overarching assumption required by many existing security protocols. Partly spurred by the growing popularity of Bitcoin, researchers have recently formalized the above view divergence problem and proposed interesting solutions (which we call view reconciliation protocols). For example, in CRYPTO 2015, Andrychowicz and Dziembowski proposed a view reconciliation protocol with Theta(N) time complexity, with N being the number of honest nodes in the system. All existing view reconciliation protocols so far have a similar Theta(N) time complexity. As this paper's main contribution, we propose a novel view reconciliation protocol with a time complexity of only Theta(frac{ln N}{lnln N}). To achieve such an exponential improvement, we aggressively exploit randomization. | Source Title: | Proceedings - IEEE INFOCOM | URI: | https://scholarbank.nus.edu.sg/handle/10635/186045 | ISBN: | 9781538641286 | ISSN: | 0743166X | DOI: | 10.1109/INFOCOM.2018.8486394 |
Appears in Collections: | Staff Publications Elements |
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