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On private Hamming distance computation
The Journal of Supercomputing, 2013Finding similarities between two datasets is an important task in many research areas, particularly those of data mining, information retrieval, cloud computing, and biometrics. However, maintaining data protection and privacy while enabling similarity measurements has become a priority for data owners in recent years.
Kok-Seng Wong, Myung Ho Kim
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On minimal Hamming compatible distances
RAIRO - Theoretical Informatics and Applications, 2014Summary: A Hamming compatible metric is an integer-valued metric on the words of a finite alphabet which agrees with the usual Hamming distance for words of equal length. We define a new Hamming compatible metric and show this metric is minimal in the class of all ``well-behaved'' Hamming compatible metrics.
Parsa Bakhtary, Othman Echi
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HAMFAST: Fast Hamming Distance Computation
2009 WRI World Congress on Computer Science and Information Engineering, 2009Similarity is a vague concept which can be treated in a quantitative manner only using appropriate mathematical representation of the objects to compare and a metric on the space representation. In biology the mathematical representation of structure relies on strings taken from an alphabet of m symbols. Very often binary strings, m = 2, are used.
Francesco Pappalardo 0001 +4 more
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A Compressed Index for Hamming Distances
2014Some instances of multimedia data can be represented as high dimensional binary vectors under the hamming distance. The standard index used to handle queries is Locality Sensitive Hashing (LSH), reducing approximate queries to a set of exact searches.
Francisco Santoyo +2 more
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On computing the Hamming distance
Acta Cybern., 2004Summary: Methods for the fast computation of the Hamming distance developed for the case of a large number of pairs of words are presented and discussed in the paper. The connection of this subject to some questions about intersecting sets and Hadamard designs is also considered.
Gerzson Kéri, Ákos Kisvölcsey
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Pattern matching in the Hamming distance with thresholds
Information Processing Letters, 2011zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mikhail J. Atallah, Timothy W. Duket
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Hamming Distance based Clustering Algorithm
International Journal of Information Retrieval Research, 2012Cluster analysis has been extensively used in machine learning and data mining to discover distribution patterns in the data. Clustering algorithms are generally based on a distance metric in order to partition the data into small groups such that data instances in the same group are more similar than the instances belonging to different groups.
Ritu Vijay +2 more
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Anonymized Distance Filter in Hamming Space
2016Search algorithms typically involve intensive distance computations and comparisons. In privacy-aware applications such as biometric identification, exposing the distance information may lead to compromise of sensitive data that have privacy and security implications.
Yi Wang 0017 +3 more
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Algorithms for the Maximum Hamming Distance Problem
2005We study the problem of finding two solutions to a constraint satisfaction problem which differ on the assignment of as many variables as possible – the Max Hamming Distance problem for CSPs – a problem which can, among other things, be seen as a domain independent way of quantifying “ignorance.” The first algorithm we present is an $\mathcal{O}(1.7338^
Ola Angelsmark, Johan Thapper
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Evolutive Tandem Repeats Using Hamming Distance
2002In this paper, we present an algorithm for detecting a "new" type of approximate repeat in texts, named evolutive tandem repeat. An evolutive tandem repeat consists in the concatenation of a series of copies, where every copy might slightly differ from its predecessor.
Groult, Richard +2 more
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