DMCA
Homomorphic Fingerprints under Misalignments: Sketching Edit and Shift Distances (2013)
| Citations: | 3 - 1 self |
BibTeX
@MISC{Andoni13homomorphicfingerprints,
author = {Alexandr Andoni and Andrew Mcgregor and et al.},
title = {Homomorphic Fingerprints under Misalignments: Sketching Edit and Shift Distances },
year = {2013}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Fingerprinting is a widely-used technique for efficiently verifying that two files are identical. More generally, linear sketching is a form of lossy compression (based on random projections) that also enables the “dissimilarity ” of nonidentical files to be estimated. Many sketches have been proposed for dissimilarity measures that decompose coordinate-wise such as the Hamming distance between alphanumeric strings, or the Euclidean distance between vectors. However, virtually nothing is known on sketches that would accommodate alignment errors. With such errors, Hamming or Euclidean distances are rendered useless: a small misalignment may result in a file that looks very dissimilar to the original file according such measures. In this paper, we present the first linear sketch that is robust to a small number of alignment errors. Specifically, the sketch can be used to determine whether two files are within a small Hamming distance of being a cyclic shift of each other. Furthermore, the sketch is homomorphic with respect to rotations: it is possible to construct the sketch of a cyclic shift of a file given only the sketch of the original file. The relevant dissimilarity measure, known as the shift distance, arises in the context of embedding edit distance and our result addressed an open problem [26, Question 13] with a rather surprising outcome. Our sketch projects a length n file into D(n) · polylog n dimensions where D(n) ≪ n is the number of divisors of n. The striking fact is that this is near-optimal, i.e., the D(n) dependence is inherent to a problem that is ostensibly about lossy compression. In contrast, we then show that any sketch for estimating the edit distance between two files, even when small, requires sketches whose size is nearly linear in n. This lower bound addresses a long-standing open problem on the low distor-
Keyphrases
shift distance homomorphic fingerprint lossy compression alignment error euclidean distance original file cyclic shift edit distance linear sketching open problem small hamming distance alphanumeric string nonidentical file first linear sketch long-standing open problem dissimilarity measure length file hamming distance small misalignment many sketch surprising outcome low distor small number polylog dimension widely-used technique sketch project relevant dissimilarity measure random projection striking fact
