Abstract
In this paper, we present a secure method of computing Pearson correction coefficients while preserving data privacy as well as data quality in the distributed computing environment. In general data analytical/mining processes, individual data owners need to provide their original data to the third parties. In many cases, however, the original data contain sensitive information, and the data owners do not want to disclose their data in the original form for the purpose of privacy preservation. In this paper, we address a problem of secure multiparty computation of Pearson correlation coefficients. For the secure Pearson correlation computation, we first propose an advanced solution by exploiting the secure scalar product. We then present an approximate solution by adopting the lower-dimensional transformation. We finally empirically show that the proposed solutions are practical methods in terms of execution time and data quality.
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Acknowledgment
This work was supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2016-0-00179, Development of an Intelligent Sampling and Filtering Techniques for Purifying Data Streams).
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Hong, SK., Gil, MS., Moon, YS. (2018). Secure Computation of Pearson Correlation Coefficients for High-Quality Data Analytics. In: Liu, C., Zou, L., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10829. Springer, Cham. https://doi.org/10.1007/978-3-319-91455-8_8
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DOI: https://doi.org/10.1007/978-3-319-91455-8_8
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