A Distributed Information Divergence Estimation over Data Streams

In this paper, we consider the setting of large scale distributed systems, in which each node needs to quickly process a huge amount of data received in the form of a stream that may have been tampered with by an adversary. In this situation, a fundamental problem is how to detect and quantify the a...

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Vydáno v:IEEE transactions on parallel and distributed systems Ročník 25; číslo 2; s. 478 - 487
Hlavní autoři: Anceaume, Emmanuelle, Busnel, Yann
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.02.2014
Institute of Electrical and Electronics Engineers
Témata:
Algorithm design and analysis
Approximation algorithms
byzantine adversary
Computational modeling
Computer Science
Data models
Data stream
Data Structures and Algorithms
Distributed, Parallel, and Cluster Computing
Entropy
Estimation
Kullback-Leibler divergence
performance analysis
Radiation detectors
randomized approximation algorithm
Randomized approximation algorithm
Data stream
Divergence
ISSN:1045-9219
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Shrnutí:In this paper, we consider the setting of large scale distributed systems, in which each node needs to quickly process a huge amount of data received in the form of a stream that may have been tampered with by an adversary. In this situation, a fundamental problem is how to detect and quantify the amount of work performed by the adversary. To address this issue, we propose a novel algorithm AnKLe for estimating the Kullback-Leibler divergence of an observed stream compared with the expected one. AnKLe combines sampling techniques and information-theoretic methods. It is very efficient, both in terms of space and time complexities, and requires only a single pass over the data stream. We show that AnKLe is an (ε, δ)-approximation algorithm with a space complexity Õ(1/ε + 1/ε 2 ) bits in "most" cases, and Õ(1/ε + (n-ε -1 )/ε 2 ) otherwise, where n is the number of distinct data items in a stream. Moreover, we propose a distributed version of AnKLe that requires at most O (rℓ (log n + 1)) bits of communication between the ℓ participating nodes, where r is number of rounds of the algorithm. Experimental results show that the estimation provided by AnKLe remains accurate even for different adversarial settings for which the quality of other methods dramatically decreases.
ISSN:1045-9219
DOI:10.1109/TPDS.2013.101