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

本研究的目的是提出一种方法，消除对信任的顺序模式挖掘（SPM），同时保护隐私，并提供安全，精确，快速的顺序数据分析，仔细学习数据分布的需要。近年来，针对无隐私保护的序列医学数据的序列数据分析的基本算法得到了研究。详细地说，已经设计和评估了几种分析来自不止一家医院的序列变异的方法。对基本的隐私保护SPM也进行了详细的研究，并观察到了初步的实验结果。为了估计序列的频率，将适量的噪声添加到原始频率以确保隐私。

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.