Privacy-preserving range query, which allows the server to implement secure and efficient range query on encrypted data, has been widely studied in recent years. Existing privacy-preserving range query schemes can realize effective range query, but usually suffer from the low efficiency and security. In order to solve the above issues, we propose an Efficient and Privacy-preserving encode-based Range Query over encrypted cloud data (namely basic EPRQ), which encodes the data and range by using Range Encode (REncoder), and then encrypts the codes via Additional Symmetric-Key Hidden Vector Encryption (ASHVE) technology. The basic EPRQ can achieve effective range query while ensuring privacy protection. Then, we split the codes to reduce the storage cost. We further propose an improved scheme, EPRQ+, which constructs a binary tree-based index to achieve faster-than-linear retrieval. Finally, our formal security analysis proves that our schemes are secure against Indistinguishability under Chosen-Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are feasible in practice, where EPRQ+ scheme improves the storage efficiency by about 4 times and the query efficiency by about 8 times compared to the basic EPRQ.

中文翻译：

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对加密的云数据进行高效且保护隐私的基于编码的范围查询


隐私保护范围查询，即允许服务器对加密数据实现安全高效的范围查询，近年来得到了广泛的研究。现有的隐私保护范围查询方案可以实现有效的范围查询，但通常存在效率和安全性低的问题。为了解决上述问题，我们提出了一种基于加密云数据的高效且隐私保护的 Range Query（即基本 EPRQ），它使用 Range Encoder（REncoder）对数据和范围进行编码，然后通过额外的对称密钥隐藏向量加密 （ASHVE） 技术对代码进行加密。基本的 EPRQ 可以在保证隐私保护的同时实现有效的范围查询。然后，我们拆分代码以降低存储成本。我们进一步提出了一种改进的方案 EPRQ+，它构建了一个基于二叉树的索引以实现更快的线性检索。最后，我们的形式化安全分析证明了我们的方案在选择明文攻击 （IND-CPA） 下是安全的，并且大量实验表明我们的方案在实践中是可行的，其中 EPRQ+ 方案与基本 EPRQ 相比，存储效率提高了约 4 倍，查询效率提高了约 8 倍。