To reduce data storage costs, more individuals are using cloud servers for reliable, scalable, cost-effective, and globally accessible solutions. However, storing data in plaintext on cloud servers can lead to data leakage risks. Moreover, the advancement of quantum computing poses a threat to traditional encryption algorithms. To counter quantum computing attacks and enable searches over encrypted keywords, lattice-based searchable encryption with conjunctive keyword search has been implemented. Nonetheless, existing schemes expose keyword fields and leaks additional information. To mitigate this, we propose a privacy-preserving method based on lattice hardness assumptions. It enables testing the existence of an encrypted keyword in a set of encrypted keywords without requiring the keyword fields. Additionally, we propose two improved methods: one for inclusion-based searches between two keyword sets, and another for range-based keyword searches. These form the basis for three lattice-based identity-based searchable encryption schemes that support disjunctive, conjunctive, and range keyword searches, respectively. The storage overhead of ciphertexts and trapdoors is unaffected by the number of keywords, making our scheme suitable for multi-keyword search scenarios. Our formal security analysis uses the learning with errors (LWE) assumption and our theoretical analysis and experimental simulations show comparable efficiency and low storage overhead.

中文翻译：

---

基于身份的加密，具有来自格的析取、合取和范围关键字搜索


为了降低数据存储成本，越来越多的人使用云服务器来获得可靠、可扩展、经济高效且全球可访问的解决方案。然而，将数据以明文形式存储在云服务器上可能会导致数据泄露风险。此外，量子计算的进步对传统加密算法构成了威胁。为了对抗量子计算攻击并实现对加密关键字的搜索，已经实现了基于格的可搜索加密和联合关键字搜索。尽管如此，现有方案暴露了关键字字段并泄漏了附加信息。为了缓解这个问题，我们提出了一种基于晶格硬度假设的隐私保护方法。它可以测试一组加密关键字中是否存在加密关键字，而无需关键字字段。此外，我们提出了两种改进的方法：一种用于两个关键字集之间基于包含的搜索，另一种用于基于范围的关键字搜索。这些构成了三种基于格的基于身份的可搜索加密方案的基础，这些方案分别支持析取、合取和范围关键字搜索。密文和陷门的存储开销不受关键字数量的影响，使得我们的方案适合多关键字搜索场景。我们的正式安全分析使用错误学习 (LWE) 假设，我们的理论分析和实验模拟显示出相当的效率和较低的存储开销。