Since ML algorithms have proven their success in many different applications, there is also a big interest in privacy preserving (PP) ML methods for building models on sensitive data. 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 ML model to the clouds providing such services. To address this, we propose a secure 3-party computation framework, CECILIA, offering PP building blocks to enable complex operations privately. In addition to the adapted and common operations like addition and multiplication, it offers multiplexer, most significant bit and modulus conversion. The first two are novel in terms of methodology and the last one is novel in terms of both functionality and methodology. CECILIA also has two complex 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 use CECILIA to realize the private inference on pre-trained RKNs, which require more complex operations than most other DNNs, on the structural classification of proteins as the first study ever accomplishing the PP inference on RKNs. In addition to the successful private computation of basic building blocks, the results demonstrate that we perform the exact and fully private exponential computation, which is done by approximation in the literature so far. Moreover, they also show that we compute the exact inverse square root of a secret Gram matrix 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 ML algorithms as well as further computations privately computable by the building blocks of the framework.

翻译：由于机器学习算法已在众多不同应用中证明了其成功性，针对敏感数据构建模型的隐私保护机器学习方法也引起了广泛关注。此外，数据源数量的增长以及这些算法所需的高计算能力，迫使个体将机器学习模型的训练和/或推理外包给提供此类服务的云端。为解决此问题，我们提出了一个安全三方计算框架CECILIA，提供隐私保护基础构件以实现复杂的隐私计算操作。除了适配的常见运算（如加法和乘法）外，该框架还提供了多路复用器、最高有效位和模数转换功能。前两者在方法论上具有新颖性，后者在功能性和方法论上均属创新。CECILIA还包含两种复杂的新型方法：公开底数以秘密值为指数的精确指数运算，以及秘密格拉姆矩阵的逆平方根计算。我们利用CECILIA首次实现了对预训练RKNs的隐私推理——这是首个完成RKNs隐私推理的研究，应用于蛋白质结构分类任务，该任务需要比大多数其他深度神经网络更复杂的运算。除了成功实现基础构件的隐私计算外，实验结果表明我们实现了精确且完全隐私的指数计算（现有文献中此类计算均采用近似方法）。此外，研究还表明我们能在特定隐私级别下精确计算秘密格拉姆矩阵的逆平方根，这在该领域文献中尚未被涉及。我们还在合成数据集上分析了CECILIA在不同设置下的可扩展性。该框架通过其基础构件，为其他机器学习算法及更广泛计算的隐私化实现展现了巨大潜力。