新型加密方法让AI直接处理加密数据，性能远超传统方案。
Technical Evaluation of a Disruptive Approach in Homomorphic AI
- 用依赖密钥的哈希函数保护数据，保留相似性特征
- 在不修改现有AI算法前提下实现加密数据处理，性能显著提升
- 适合关注隐私计算与安全推理的开发者和研究者
我们对一种新型颠覆性密码学方法——基于哈希的同态人工智能（HbHAI）进行了技术评估。HbHAI 基于一类新型密钥依赖哈希函数，天然保持了大多数人工智能算法所依赖的相似性属性。核心主张是：无需修改现有原生AI算法，即可在加密状态下分析和处理数据，性能远超现有同态加密方案。本文通过传统无监督与有监督学习方法（聚类、分类、深度神经网络）对多个受HbHAI保护的数据集（非公开预览）进行测试，采用现成的、未经修改的AI算法进行独立分析。结果验证了其安全性、可操作性及性能主张，仅存在少量次要保留意见。
原文摘要 · Abstract (English)
We present a technical evaluation of a new, disruptive cryptographic approach to data security, known as HbHAI (Hash-based Homomorphic Artificial Intelligence). HbHAI is based on a novel class of key-dependent hash functions that naturally preserve most similarity properties, most AI algorithms rely on. As a main claim, HbHAI makes now possible to analyze and process data in its cryptographically secure form while using existing native AI algorithms without modification, with unprecedented performances compared to existing homomorphic encryption schemes. We tested various HbHAI-protected datasets (non public preview) using traditional unsupervised and supervised learning techniques (clustering, classification, deep neural networks) with classical unmodified AI algorithms. This paper presents technical results from an independent analysis conducted with those different, off-the-shelf AI algorithms. The aim was to assess the security, operability and performance claims regarding HbHAI techniques. As a results, our results confirm most these claims, with only a few minor reservations.
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