Secure, Precise and Fast Sequential Pattern Mining with Learning Data Distribution

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This study aims to present a method for eliminating the need for trust in sequential pattern mining (SPM) while preserving privacy and providing secure, precise, and fast sequential data analysis which carefully learns the data distribution. The fundamental algorithms of sequential data analysis on sequential medical data without privacy-preserving have been studied this year. In detail, several methods to analyze the sequence variants from more than one hospital have been designed and evaluated. The basic privacy-preserving SPM has also been studied in detail and the initial experimental results have been observed. For estimating the frequency of the sequences, an appropriate amount of noise is added to the original frequency to ensure privacy.