A space- and time-efficient Implementation of the Merkle Tree Traversal Algorithm
Markus Knecht, Willi Meier, Carlo U. Nicola
Abstract
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Markus Knecht, Willi Meier, Carlo U. Nicola
Abstract
Open-access reader
We present an algorithm for the Merkle tree traversal problem which combines the efficient space-time trade-off from the fractal Merkle tree [3] and the space efficiency from the improved log space-time Merkle trees traversal [8]. We give an exhaustive analysis of the space and time efficiency of our algorithm in function of the parameters H (the height of the Merkle tree) and h (h = H L where L is the number of levels in the Merkle tree). We also analyze the space impact when a continuous deterministic pseudo-random number generator (PRNG) is used to generate the leaves. We further program a low storage-space and a low time-overhead version of the algorithm in Java and measure its performance with respect to the two different implementations cited above. Our implementation uses the least space when a continuous PRNG is used for the leaf calculation.
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We present an algorithm for the Merkle tree traversal problem which combines the efficient space-time trade-off from the fractal Merkle tree [3] and the space efficiency from the improved log space-time Merkle trees traversal [8]. We give an exhaustive analysis of the space and time efficiency of our algorithm in function of the parameters H (the height of the Merkle tree) and h (h = H L where L is the number of levels in the Merkle tree). We also analyze the space impact when a continuous deterministic pseudo-random number generator (PRNG) is used to generate the leaves. We further program a low storage-space and a low time-overhead version of the algorithm in Java and measure its performance with respect to the two different implementations cited above. Our implementation uses the least space when a continuous PRNG is used for the leaf calculation.
Key concepts: Tree traversal, Merkle tree, Tree (set theory), Algorithm, Computer science, Overhead (engineering), Mathematics, Parallel computing