Various approaches to privacy-preserving machine learning (PPML) using Fully Homomorphic Encryption (FHE) have been developed, focusing on secure data outsourcing to untrusted servers by data owners. While FHE enables computation on encrypted data, it faces significant limitations, particularly in integrating control structures like decision expressions and conditional statements, which are vital in standard programming. For instance, tasks like selecting the smallest value from an encrypted list for feature selection in decision trees are challenging due to the inability to evaluate comparison expressions in encrypted form. Most existing literature on FHE have concentrated on encrypted prediction using pre-trained models due to these challenges, with training processes often requiring Intermediate Rounds of Decryption and Evaluation (IRDE). IRDE involves interactive communication where the potentially untrusted server performs encrypted computations, while the client handles control structures by decrypting and evaluating data in plaintext. While it presents a solution to the control structure problem in encrypted programming, IRDE protocols go against FHE's principles of building truly encrypted programs as portions of such programs must leave the encrypted space (untrusted server) and be executed on the trusted client, who holds the private keys for decryption. Such models, however efficiently they can be made, would be inferior to models that eliminate the need for IRDE all-together. The ability to remove IRDE allows both computation and control structures to be performed on untrusted servers without requiring trusted clients for multiple IRDE cycles. This paper introduces the Blind Evaluation Framework (BEF), a cryptographically secure programming framework enabling the execution of control structures in encrypted space without evaluating conditional expressions...

翻译：针对隐私保护机器学习（PPML）的各种方法已利用完全同态加密（FHE）开发，主要聚焦于数据所有者将数据安全外包至不可信服务器。尽管FHE支持对加密数据进行计算，但其面临显著限制，特别是在整合标准编程中至关重要的控制结构（如决策表达式和条件语句）时。例如，在决策树的特征选择中，从加密列表中选取最小值等任务因无法以加密形式评估比较表达式而极具挑战性。由于这些难题，现有关于FHE的文献多集中于使用预训练模型的加密预测，而训练过程通常需要中间解密与评估轮次（IRDE）。IRDE涉及交互式通信：潜在不可信的服务器执行加密计算，而客户端通过解密数据并以明文形式评估来负责控制结构。尽管IRDE为加密编程中的控制结构问题提供了解决方案，但该协议违背了FHE构建真正加密程序的原则——此类程序的部分内容必须脱离加密空间（不可信服务器）并在持有解密私钥的可信客户端上执行。无论这类模型能实现多高的效率，它们都劣于完全消除IRDE需求的模型。消除IRDE的能力使得计算和控制结构均可在不可信服务器上执行，无需依赖可信客户端进行多次IRDE循环。本文提出盲评估框架（BEF），这是一个密码学安全的编程框架，无需评估条件表达式即可在加密空间中执行控制结构……