Ranked keyword search over encrypted data has been extensively studied in cloud computing as it enables data users to find the most relevant results quickly. However, existing ranked multi-keyword search solutions cannot achieve efficient ciphertext search and dynamic updates with forward security simultaneously. To solve the above problems, we first present a basic Machine Learning-based Ranked Keyword Search (ML-RKS) scheme in the static setting by using the k-means clustering algorithm and a balanced binary tree. ML-RKS reduces the search complexity without sacrificing the search accuracy, but is still vulnerable to forward security threats when applied in the dynamic setting. Then, we propose an Enhanced ML-RKS (called ML-RKS $^{+}$ ) scheme by introducing a permutation matrix. ML-RKS $^{+}$ prevents cloud servers from making search queries over newly added files via previous tokens, thereby achieving forward security. The security analysis proves that our schemes protect the privacy of indexes, query tokens and keywords. Empirical experiments using the real-world dataset demonstrate that our schemes are efficient and feasible in practical applications.

中文翻译：

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通过机器学习方法对加密云数据进行关键词排名搜索


对加密数据的排名关键词搜索在云计算中得到了广泛的研究，因为它使数据用户能够快速找到最相关的结果。然而，现有的排序多关键词搜索解决方案无法同时实现高效的密文搜索和具有前向安全性的动态更新。为了解决上述问题，我们首先使用 k-means 聚类算法和平衡二叉树在静态设置中提出一种基本的基于机器学习的排名关键词搜索（ML-RKS）方案。 ML-RKS在不牺牲搜索精度的情况下降低了搜索复杂度，但在动态设置中应用时仍然容易受到转发安全威胁。然后，我们通过引入置换矩阵提出了增强型 ML-RKS（称为 ML-RKS $^{+}$ ）方案。 ML-RKS$^{+}$防止云服务器通过之前的令牌对新添加的文件进行搜索查询，从而实现前向安全。安全分析证明我们的方案保护了索引、查询令牌和关键字的隐私。使用真实世界数据集的实证实验表明，我们的方案在实际应用中是有效且可行的。