Showing papers on "Locality-sensitive hashing published in 2000"
Journal Article•
TL;DR: It is shown that for various choices of the parameters in the SL 2 (IF 2n ) hashing scheme, suggested by Tillich and Zemor, messages can be modified without changing the hash value.
Abstract: We show that for various choices of the parameters in the SL 2 (IF 2n ) hashing scheme, suggested by Tillich and Zemor, messages can be modified without changing the hash value. Moreover, examples of hash functions with a trapdoor within this family are given. Due to these weaknesses one should impose at least certain restrictions on the allowed parameter values when using the SL 2 (IF 2n ) hashing scheme for cryptographic purposes.
10 citations
Proceedings Article•
10 Jul 2000
TL;DR: An evolutionary algorithm to locate efficient hashing functions for specific data sets by sampling and evolving from the set of polynomials is presented, showing consistently better performance than other common hashing methods.
Abstract: Hashing is an efficient method for storage and retrieval of large amounts of data. Presented here is an evolutionary algorithm to locate efficient hashing functions for specific data sets by sampling and evolving from the set of polynomials. Functions derived in this way-show consistently better performance than other common hashing methods, and indicate the power of evolutionary algorithms in search and retrieval.
10 citations
06 Nov 2000
TL;DR: Three subseries search algorithms are proposed and compared with the naive method that sequen tially scans the whole data set, as well as a method adapted from a state-of-art sub series search algorithm.
Abstract: Searc hingfor nearest neigh b orsin a large set of time series is an importan tdata mining task. This paper studies the following type of time series nearest neighbor queries: Given a query series and a starting time, among all the subseries (of a collection of data series) that have the same length as the query series and start at the given time, nd the K subseries that are closest to the query series. T o support such queries, the paper develops a tec hnique that uses a xed number of values to approximate each whole data series, and obtains the appro ximationof an y required subseries at the query time. The paper then proposes three subseries search algorithms and compares them with the naive method that sequen tially scans the whole data set, as well as a method adapted from a state-of-art subseries search algorithm. Experiments are conducted on both a real-life data set and a synthetic one. Results show that the proposed methods access only a small portion of the precise data and outperform the others in run time.
5 citations