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| URI | http://purl.tuc.gr/dl/dias/1C4CF2E5-1B4C-471E-8E48-469FDE16C3F7 | - |
| Αναγνωριστικό | http://www.softnet.tuc.gr/~minos/Papers/icde07amfm.pdf | - |
| Αναγνωριστικό | https://doi.org/10.1109/ICDE.2007.368958 | - |
| Γλώσσα | en | - |
| Μέγεθος | 10 pages | en |
| Τίτλος | Proof sketches: verifiable in-network aggregation | en |
| Δημιουργός | Garofalakis Minos | en |
| Δημιουργός | Γαροφαλακης Μινως | el |
| Δημιουργός | Hellerstein Joseph M. | en |
| Δημιουργός | Maniatis Petros | en |
| Εκδότης | Institute of Electrical and Electronics Engineers | en |
| Περίληψη | Recent work on distributed, in-network aggregation assumes a benign population of participants. Unfortunately, modern distributed systems are plagued by malicious participants. In this paper we present a first step towards verifiable yet efficient distributed, in-network aggregation in adversarial settings. We describe a general framework and threat model for the problem and then present proof sketches, a compact verification mechanism that combines cryptographic signatures and Flajolet-Martin sketches to guarantee acceptable aggregation error bounds with high probability. We derive proof sketches for count aggregates and extend them for random sampling, which can be used to provide verifiable approximations for a broad class of dataanalysis queries, e.g., quantiles and heavy hitters. Finally, we evaluate the practical use of proof sketches, and observe that adversaries can often be reduced to much smaller violations in practice than our worst-case bounds suggest. | en |
| Τύπος | Πλήρης Δημοσίευση σε Συνέδριο | el |
| Τύπος | Conference Full Paper | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2015-11-30 | - |
| Ημερομηνία Δημοσίευσης | 2007 | - |
| Θεματική Κατηγορία | Data engineering | en |
| Θεματική Κατηγορία | Databases management | en |
| Βιβλιογραφική Αναφορά | M. Garofalakis, J. M. Hellerstein and P. Maniatis, "Proof sketches: verifiable in-network aggregation", in IEEE 23rd International Conference on Data Engineering, 2007, pp. 996-1005. doi: 10.1109/ICDE.2007.368958 | en |
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