As a bridge between cloud computing platforms and the Internet of Things (IoT) devices, edge computing provides various on-demand data services to reduce latency and network congestion. To ensure data security in edge computing, sensitive data should be encrypted before being outsourced to edge servers. Public key encryption with keyword search (PEKS) can provide search service for encrypted data. Nevertheless, most existing PEKS schemes are susceptible to quantum attacks because their security assumptions are based on traditional hardness assumptions. Moreover, many lattice-based PEKS schemes only apply to the single-user scenario, limiting the range of applications. In this paper, we present an efficient lattice-based public key searchable encryption with fine-grained access control for edge computing. Our scheme achieves post-quantum security and highly flexible access control policies for multi-user applications, by utilizing the learning with errors (LWE) assumption and subset predicate encryption. The security proof illustrates that our proposed scheme is secure under chosen plaintext attacks and chosen keyword attacks. Finally, extensive experiments on real-world datasets illustrate that our encryption algorithm is faster than existing approaches.

作为云计算平台和物联网设备之间的桥梁，边缘计算提供各种按需数据服务，以减少延迟和网络拥塞。为了确保边缘计算中的数据安全，敏感数据在外包给边缘服务器之前应该进行加密。带有关键字搜索的公钥加密（PEKS）可以为加密数据提供搜索服务。然而，大多数现有的 PEKS 方案容易受到量子攻击，因为它们的安全假设基于传统的硬度假设。此外，许多基于格的PEKS方案仅适用于单用户场景，限制了应用范围。在本文中，我们提出了一种有效的基于格的公钥可搜索加密，具有用于边缘计算的细粒度访问控制。我们的方案通过利用错误学习（LWE）假设和子集谓词加密，实现了多用户应用程序的后量子安全性和高度灵活的访问控制策略。安全证明表明，我们提出的方案在选择明文攻击和选择关键字攻击下是安全的。最后，对真实世界数据集的大量实验表明，我们的加密算法比现有方法更快。