In intelligent edge computing, the private data of users are partly or entirely outsourced to the intelligent edge nodes for processing. To enforce security and privacy on edge network, data owners encrypt their private data and outsource it. It is particularly important to carry out efficient search and update on encrypted data. In this paper, we propose a searchable scheme to solve these problems on the intelligent edge network. Specially, we first propose S-HashMap index structure to make data update efficiently and safely while promising multi-keyword fuzzy ciphertext retrieval. Secondly, to measure the similarity between the query vector and the index nodes, we utilize the secure k-nearest neighbor to calculate the Euclidean distance. In addition to eliminating the necessity of pre-defined dictionaries, we achieve efficient multi-keyword fuzzy search and index updating without increasing search complexity. At the same time, this paper makes a thorough theoretical analysis and simulation of the proposed scheme. Compared with other schemes, we demonstrate that our scheme has better security and efficiency.

在智能边缘计算中，用户的私有数据部分或全部外包给智能边缘节点进行处理。为了加强边缘网络的安全性和隐私性，数据所有者对自己的私有数据进行加密并将其外包。对加密数据进行有效的搜索和更新尤为重要。在本文中，我们提出了一种可搜索的方案来解决智能边缘网络上的这些问题。特别地，我们首先提出S-HashMap索引结构，以在保证多关键字模糊密文检索的同时，有效且安全地更新数据。其次，为了测量查询向量和索引节点之间的相似性，我们利用安全的k最近邻来计算欧几里得距离。除了消除预定义词典的必要性之外，我们在不增加搜索复杂度的情况下实现了有效的多关键字模糊搜索和索引更新。同时，本文对提出的方案进行了透彻的理论分析和仿真。与其他方案相比，我们证明了我们的方案具有更好的安全性和效率。