Outsourcing big data to cloud servers has gained prominence, and growing concerns about privacy, alongside privacy-related regulations, underscore the need to encrypt data before sending them to the cloud. Nevertheless, encryption significantly hampers the query capabilities of data, particularly in the case of vertically distributed data. This paper focuses on developing secure and efficient similarity query schemes for vertically distributed data in cloud environments. As is known, current solutions are constrained by limitations in query efficiency, approximate query results, and their ability to support vertical data. To address these issues, we introduce two novel schemes: a Fast Similarity Query Scheme (FSQ) and a Non-interactive Similarity Query Scheme (NoSQ) for outsourced distributed data. In the FSQ scheme, we enhance query efficiency by designing a trusted execution environment (TEE) assisted fast secret sharing (FSS) scheme and a series of FSS-based private algorithms, enabling secure data index construction and fast similarity query processing. For the NoSQ scheme, we eliminate communication overheads by designing a TEE assisted non-interactive secret sharing (NoSS) scheme and a series of NoSS-based private algorithms. Both schemes have undergone rigorous security validation using a simulation-based real/ideal worlds model, and their efficiency has been confirmed through comprehensive experiments.

中文翻译：

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通过 TEE 增强型云计算对垂直分布数据进行安全相似性查询


将大数据外包到云服务器已引起人们的重视，人们对隐私的担忧日益增加，以及与隐私相关的法规，强调了在将数据发送到云端之前对其进行加密的必要性。然而，加密极大地阻碍了数据的查询能力，特别是在垂直分布的数据的情况下。本文重点针对云环境中垂直分布的数据开发安全高效的相似性查询方案。众所周知，当前的解决方案受到查询效率、近似查询结果及其支持垂直数据的能力方面的限制。为了解决这些问题，我们引入了两种新颖的方案：针对外包分布式数据的快速相似性查询方案（FSQ）和非交互式相似性查询方案（NoSQ）。在FSQ方案中，我们通过设计可信执行环境（TEE）辅助的快速秘密共享（FSS）方案和一系列基于FSS的私有算法来提高查询效率，从而实现安全的数据索引构建和快速相似性查询处理。对于NoSQ方案，我们通过设计TEE辅助的非交互式秘密共享（NoSS）方案和一系列基于NoSS的私有算法来消除通信开销。这两种方案都使用基于模拟的真实/理想世界模型进行了严格的安全验证，并且其效率已通过综合实验得到证实。