Since machine learning algorithms have proven their success in data mining tasks, the data with sensitive information enforce privacy preserving machine learning algorithms to emerge. Moreover, the increase in the number of data sources and the high computational power required by those algorithms force individuals to outsource the training and/or the inference of a machine learning model to the clouds providing such services. To address this dilemma, we propose a secure 3-party computation framework, CECILIA, offering privacy preserving building blocks to enable more complex operations privately. Among those building blocks, we have two novel methods, which are the exact exponential of a public base raised to the power of a secret value and the inverse square root of a secret Gram matrix. We employ CECILIA to realize the private inference on pre-trained recurrent kernel networks, which require more complex operations than other deep neural networks such as convolutional neural networks, on the structural classification of proteins as the first study ever accomplishing the privacy preserving inference on recurrent kernel networks. The results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, we can also perform the exact inverse square root of a secret Gram matrix computation up to a certain privacy level, which has not been addressed in the literature at all. We also analyze the scalability of CECILIA to various settings on a synthetic dataset. The framework shows a great promise to make other machine learning algorithms as well as further computations privately computable by the building blocks of the framework.

该研究提出了一个安全的三方计算框架CECILIA，它提供了隐私保护的机器学习方法来对敏感数据进行建模。该框架不仅提供了加法和乘法等常见运算，还提供了多路复用器、最高位和模转换等新颖的方法，并成功地实现了精确和完全私有的指数计算，并可扩展到多种设置中。同时，该研究还成功地在结构分类蛋白质的任务上实现了PP推理。

CECILIA: 全面安全的机器学习框架