Design and Security Analysis of Cryptographic Protocol for Privacy-Preserving Data Mining

在数据挖掘过程中，由于我们需要处理健康数据、个人偏好等，所以存在隐私泄露问题。隐私保护数据挖掘的方法是在原始数据中添加随机噪声数据，或者在不破坏原始数据结构的情况下进行计算和通信。在本研究中，我们设计了密码协议，从理论上证明了协议的安全性。对于安全模型，我们使用通用可组合性(UC)，其中可以安全地组合几个协议。我们设计了数据挖掘的不经意传输等协议，其安全性只有在以前的安全模型中才被证明，并在UC的概念下证明了其安全性。

In the process of data mining, there exists a problem about privacy breach because we need to deal with health data, personal preferences, etc. There are approaches to privacy preserving data mining in which random noisy data are added to the original data or computation and communication are performed without breaking the structure of original data. In this research, we designed cryptographic protocols whose security can be proved in a theoretical way. For the security model, we use Universal Composability (UC) in which combining several protocols can be done securely. We designed protocols such as oblivious transfer for data mining whose security was proved only in the previous security model, and proved the security in the notion of UC.