论文标题
她的：同态加密的表示搜索
HERS: Homomorphically Encrypted Representation Search
论文作者
论文摘要
我们提出了一种用于针对加密域中大型画廊搜索探针（或查询）图像表示的方法。我们要求用固定长度表示表示探针和画廊图像，这对于从学习的网络获得的表示是典型的。我们的加密方案对如何获得固定长度表示不可知，因此可以应用于任何应用域中的任何固定长度表示。我们的方法被称为她的方法（同源加密的表示搜索），是通过（i）压缩其估计的固有维度的表示，而准确性最小的损失（ii）使用拟议的完全同质的同质加密方案和（iii）有效地搜索加密的代理，可以将压缩的代表加密，并具有加密的代表性，并具有加密的代表性，并具有加密的代表性，并将其直接分别分别分配为累积的代表。像Imagenet这样的大型面部，指纹和对象数据集的数值结果表明，在加密域中，首次准确且快速的图像搜索是可行的（500秒； $ 275 \ times $ 275 \ times $ speed远远超过了先进的搜索，以抵制1亿个画廊）。代码可从https://github.com/human-analysis/hers-ecrypted-image-search获得。
We present a method to search for a probe (or query) image representation against a large gallery in the encrypted domain. We require that the probe and gallery images be represented in terms of a fixed-length representation, which is typical for representations obtained from learned networks. Our encryption scheme is agnostic to how the fixed-length representation is obtained and can therefore be applied to any fixed-length representation in any application domain. Our method, dubbed HERS (Homomorphically Encrypted Representation Search), operates by (i) compressing the representation towards its estimated intrinsic dimensionality with minimal loss of accuracy (ii) encrypting the compressed representation using the proposed fully homomorphic encryption scheme, and (iii) efficiently searching against a gallery of encrypted representations directly in the encrypted domain, without decrypting them. Numerical results on large galleries of face, fingerprint, and object datasets such as ImageNet show that, for the first time, accurate and fast image search within the encrypted domain is feasible at scale (500 seconds; $275\times$ speed up over state-of-the-art for encrypted search against a gallery of 100 million). Code is available at https://github.com/human-analysis/hers-encrypted-image-search
