隨著資訊科技的日益發展之下，可以產生資料的應用程式不斷地推出，但是資料中通常會包含個人之敏感資訊，這也使得隱私保護資料探勘 (Privacy Preserving Data Mining, PPDM) 的重要性逐漸提升。過往學者們提出了許多PPDM方法以保護隱私，從中得知至今尚未有一種PPDM方法能適用在所有情境。經由文獻探討得知，PPDM有一種作法為使用同態加密的方式達到隱私保護的要求，且通常都應用在羅吉斯回歸上，然而大多數文獻皆採用Sigmoid函數，發現了在效率、效能方面表現不夠好，因此本研究也以此為出發點，使用反正切函數進行同態加密的羅吉斯回歸方法。本研究提出了一套PPDM流程，為資料擁有者(甲方)透過同態加密方式保護原始資料，接著將受保護之資料傳給資料接收者(乙方)使用羅吉斯回歸進行分析處理後，最後提供此模型給乙方進行後續預測新資料之用。在生成模型之後，藉由與過往文獻所提出的方法進行比較，以分類正確率差異、預測不一致比例以及訓練時間等評估方式，評估本研究之模型，並在實驗之後發現，本研究在大部分的情形下，此三個評估方式表現皆較佳。

As information technology advances, the data collected by applications often contain sensitive personal information, which makes Privacy Preserving Data Mining (PPDM) increasingly important. Scholars have proposed many PPDM methods to protect privacy, but no single PPDM method that can be applied in all domains. Using homomorphic encryption to achieve the privacy protection requirement is an approach in PPDM. Logistic regression with Sigmoid function is usually adopted in applying homomorphic encryption, while its computational cost and is high and the frequency of prediction inconsistency is not small. To overcome such deficiencies, this study proposes a way to introduce the arctangent function for logistic regression so that the data owner can protect the original data from the receiver by applying homomorphic encryption. The experimental results on 20 data sets show that the method proposed by this study not only is more efficient, but also can reduce prediction inconsistency on average.

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