论文标题
隐私保护分布式机器学习速度更快
Privacy-Preserving Distributed Machine Learning Made Faster
论文作者
论文摘要
随着机器学习的开发，单个服务器很难处理所有数据。因此，机器学习任务需要分布在多个服务器上，从而将集中式的机器学习变成分布式的机器学习。但是，在分布式机器学习中，隐私仍然是一个未解决的问题。多键同态加密是解决该问题的合适候选者之一。但是，多键同态加密方案（MKTFHE）的最新结果仅支持NAND门。尽管图灵完成了，但它需要对NAND门的有效封装才能进一步支持数学计算。本文准确地设计和实施了一系列关于正整数的操作。首先，我们设计基本的自举门，其效率与NAND门的效率相同。其次，我们基于基本的二进制自举门来构建实用的$ k $ bit补充数学运算符。构造的创建可以在正整数和负整数上执行加法，减法，乘法和除法。最后，我们通过实现了分布式隐私的机器学习算法，即使用两种不同的解决方案的线性回归来证明设计运营商的通用性。实验表明，我们设计的运营商是实用和高效的。
With the development of machine learning, it is difficult for a single server to process all the data. So machine learning tasks need to be spread across multiple servers, turning the centralized machine learning into a distributed one. However, privacy remains an unsolved problem in distributed machine learning. Multi-key homomorphic encryption is one of the suitable candidates to solve the problem. However, the most recent result of the Multi-key homomorphic encryption scheme (MKTFHE) only supports the NAND gate. Although it is Turing complete, it requires efficient encapsulation of the NAND gate to further support mathematical calculation. This paper designs and implements a series of operations on positive and negative integers accurately. First, we design basic bootstrapped gates with the same efficiency as that of the NAND gate. Second, we construct practical $k$-bit complement mathematical operators based on our basic binary bootstrapped gates. The constructed created can perform addition, subtraction, multiplication, and division on both positive and negative integers. Finally, we demonstrated the generality of the designed operators by achieving a distributed privacy-preserving machine learning algorithm, i.e. linear regression with two different solutions. Experiments show that the operators we designed are practical and efficient.
