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An Optimization Method for Performance Improvement of Set-based Similar Sequence Matching
http://doi.org/10.5626/JOK.2018.45.4.403
The set-based similar sequence matching method involves searching for data set sequences that are similar to a query set sequence. In the method, the similarity between two sets is represented as the size of intersection between them. However, there is a critical performances issue for calculating intersection size if the number of sets is large. In the past, authors of the present work proposed a method to improve the performance of set-based similar sequence matching using simple index structure. In this paper, we propose an optimization method for more efficient running of set-based similar sequence matching. Our method is based on pruning that excludes unnecessary calculation. Through experiments, we show that the proposed method reduces the execution time by about 20% compared to the existing methods.
Secure Multi-Party Computation of Correlation Coefficients
Sun Kyong Hong, Sang Pil Kim, Hyo Sang Lim, Yang Sae Moon
In this paper, we address the problem of computing Pearson correlation coefficients and Spearman’s rank correlation coefficients in a secure manner while data providers preserve privacy of their own data in distributed environment. For a data mining or data analysis in the distributed environment, data providers(data owners) need to share their original data with each other. However, the original data may often contain very sensitive information, and thus, data providers do not prefer to disclose their original data for preserving privacy. In this paper, we formally define the secure correlation computation, SCC in short, as the problem of computing correlation coefficients in the distributed computing environment while preserving the data privacy (i.e., not disclosing the sensitive data) of multiple data providers. We then present SCC solutions for Pearson and Spearman’s correlation coefficients using secure scalar product. We show the correctness and secure property of the proposed solutions by presenting theorems and proving them formally. We also empirically show that the proposed solutions can be used for practical applications in the performance aspect.
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